Threat mitigation system and method

ABSTRACT

A computer-implemented method, computer program product and computing system for: receiving platform information from a plurality of security-relevant subsystems; processing the platform information to generate processed platform information; identifying less threat-pertinent content included within the processed content; and routing the less threat-pertinent content to a long term storage system.

RELATED APPLICATION(S)

This application claims the benefit of the following U.S. ProvisionalApplication Nos. 62/681,279, filed on 6 Jun. 2018; 62/737,558, filed on27 Sep. 2018; and 62/817,943 filed on 13 Mar. 2019, their entirecontents of which are herein incorporated by reference.

TECHNICAL FIELD

This disclosure relates to threat mitigation systems and, moreparticularly, to threat mitigation systems that utilize ArtificialIntelligence (AI) and Machine Learning (ML).

BACKGROUND

In the computer world, there is a constant battle occurring between badactors that want to attack computing platforms and good actors who tryto prevent the same. Unfortunately, the complexity of such computerattacks in constantly increasing, so technology needs to be employedthat understands the complexity of these attacks and is capable ofaddressing the same. Additionally, the use of Artificial Intelligence(AI) and Machine Learning (ML) has revolutionized the manner in whichlarge quantities of content may be processed so that information may beextracted that is not readily discernible to a human user. Accordinglyand though the use of AI/ML, the good actors may gain the upper hand inthis never ending battle.

SUMMARY OF DISCLOSURE Concept 19)

In one implementation, a computer-implemented method is executed on acomputing device and includes: receiving platform information from aplurality of security-relevant subsystems; processing the platforminformation to generate processed platform information; identifying lessthreat-pertinent content included within the processed content; androuting the less threat-pertinent content to a long term storage system.

One or more of the following features may be included. Processing theplatform information to generate processed platform information mayinclude: parsing the platform information into a plurality ofsubcomponents to allow for compensation of varying formats and/ornomenclature. Processing the platform information to generate processedplatform information may include: enriching the platform information byincluding supplemental information from external information resources.Processing the platform information to generate processed platforminformation may include: utilizing artificial intelligence/machinelearning to identify one or more patterns/behaviors defined within theplatform information. The plurality of security-relevant subsystems mayinclude one or more of: a data lake; a data log; a security-relevantsoftware application; a security-relevant hardware system; and aresource external to the computing platform. Identifying lessthreat-pertinent content included within the processed content mayinclude: processing the processed content to identify non-actionableprocessed content that is not usable by a threat analysis engine. Athird-party may be allowed to access and search the long term storagesystem.

In another implementation, a computer program product resides on acomputer readable medium and has a plurality of instructions stored onit. When executed by a processor, the instructions cause the processorto perform operations including: receiving platform information from aplurality of security-relevant subsystems; processing the platforminformation to generate processed platform information; identifying lessthreat-pertinent content included within the processed content; androuting the less threat-pertinent content to a long term storage system.

One or more of the following features may be included. Processing theplatform information to generate processed platform information mayinclude: parsing the platform information into a plurality ofsubcomponents to allow for compensation of varying formats and/ornomenclature. Processing the platform information to generate processedplatform information may include: enriching the platform information byincluding supplemental information from external information resources.Processing the platform information to generate processed platforminformation may include. utilizing artificial intelligence/machinelearning to identify one or more patterns/behaviors defined within theplatform information. The plurality of security-relevant subsystems mayinclude one or more of: a data lake; a data log; a security-relevantsoftware application; a security-relevant hardware system; and aresource external to the computing platform. Identifying lessthreat-pertinent content included within the processed content mayinclude: processing the processed content to identify non-actionableprocessed content that is not usable by a threat analysis engine. Athird-party may be allowed to access and search the long term storagesystem.

In another implementation, a computing system includes a processor andmemory is configured to perform operations including: receiving platforminformation from a plurality of security-relevant subsystems; processingthe platform information to generate processed platform information;identifying less threat-pertinent content included within the processedcontent; and routing the less threat-pertinent content to a long termstorage system.

One or more of the following features may be included. Processing theplatform information to generate processed platform information mayinclude: parsing the platform information into a plurality ofsubcomponents to allow for compensation of varying formats and/ornomenclature. Processing the platform information to generate processedplatform information may include: enriching the platform information byincluding supplemental information from external information resources.Processing the platform information to generate processed platforminformation may include: utilizing artificial intelligence/machinelearning to identify one or more patterns/behaviors defined within theplatform information. The plurality of security-relevant subsystems mayinclude one or more of: a data lake; a data log; a security-relevantsoftware application; a security-relevant hardware system; and aresource external to the computing platform. Identifying lessthreat-pertinent content included within the processed content mayinclude: processing the processed content to identify non-actionableprocessed content that is not usable by a threat analysis engine. Athird-party may be allowed to access and search the long term storagesystem.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features andadvantages will become apparent from the description, the drawings, andthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic view of a distributed computing networkincluding a computing device that executes a threat mitigation processaccording to an embodiment of the present disclosure;

FIG. 2 is a diagrammatic view of an exemplary probabilistic modelrendered by a probabilistic process of the threat mitigation process ofFIG. 1 according to an embodiment of the present disclosure;

FIG. 3 is a diagrammatic view of the computing platform of FIG. 1according to an embodiment of the present disclosure;

FIG. 4 is a flowchart of an implementation of the threat mitigationprocess of FIG. 1 according to an embodiment of the present disclosure;

FIGS. 5-6 are diagrammatic views of screens rendered by the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIGS. 7-9 are flowcharts of other implementations of the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIG. 10 is a diagrammatic view of a screen rendered by the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIG. 11 is a flowchart of another implementation of the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIG. 12 is a diagrammatic view of a screen rendered by the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIG. 13 is a flowchart of another implementation of the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIG. 14 is a diagrammatic view of a screen rendered by the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIG. 15 is a flowchart of another implementation of the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIG. 16 is a diagrammatic view of screens rendered by the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIGS. 17-23 are flowcharts of other implementations of the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIG. 24 is a diagrammatic view of a screen rendered by the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure; and

FIGS. 25-30 are flowcharts of other implementations of the threatmitigation process of FIG. 1 according to an embodiment of the presentdisclosure.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS System Overview

Referring to FIG. 1, there is shown threat mitigation process 10. Threatmitigation process 10 may be implemented as a server-side process, aclient-side process, or a hybrid server-side/client-side process. Forexample, threat mitigation process 10 may be implemented as a purelyserver-side process via threat mitigation process 10 s. Alternatively,threat mitigation process 10 may be implemented as a purely client-sideprocess via one or more of threat mitigation process 10 cl, threatmitigation process 10 c 2, threat mitigation process 10 c 3, and threatmitigation process 10 c 4. Alternatively still, threat mitigationprocess 10 may be implemented as a hybrid server-side/client-sideprocess via threat mitigation process 10 s in combination with one ormore of threat mitigation process 10 cl, threat mitigation process 10 c2, threat mitigation process 10 c 3, and threat mitigation process 10 c4. Accordingly, threat mitigation process 10 as used in this disclosuremay include any combination of threat mitigation process 10 s, threatmitigation process 10 c 1, threat mitigation process 10 c 2, threatmitigation process, and threat mitigation process 10 c 4.

Threat mitigation process 10 s may be a server application and mayreside on and may be executed by computing device 12, which may beconnected to network 14 (e.g., the Internet or a local area network).Examples of computing device 12 may include, but are not limited to: apersonal computer, a laptop computer, a personal digital assistant, adata-enabled cellular telephone, a notebook computer, a television withone or more processors embedded therein or coupled thereto, acable/satellite receiver with one or more processors embedded therein orcoupled thereto, a server computer, a series of server computers, a minicomputer, a mainframe computer, or a cloud-based computing network.

The instruction sets and subroutines of threat mitigation process 10 s,which may be stored on storage device 16 coupled to computing device 12,may be executed by one or more processors (not shown) and one or morememory architectures (not shown) included within computing device 12.Examples of storage device 16 may include but are not limited to: a harddisk drive: a RAID device; a random access memory (RAM); a read-onlymemory (ROM); and all forms of flash memory storage devices.

Network 14 may be connected to one or more secondary networks (e.g.,network 18), examples of which may include but are not limited to: alocal area network; a wide area network; or an intranet, for example.

Examples of threat mitigation processes 10 cl, 10 c 2, 10 c 3, 10 c 4may include but are not limited to a client application, a web browser,a game console user interface, or a specialized application (e.g., anapplication running on e.g., the Android™ platform or the iOS™platform). The instruction sets and subroutines of threat mitigationprocesses 10 cl, 10 c 2, 10 c 3, 10 c 4, which may be stored on storagedevices 20, 22, 24, 26 (respectively) coupled to client electronicdevices 28, 30, 32, 34 (respectively), may be executed by one or moreprocessors (not shown) and one or more memory architectures (not shown)incorporated into client electronic devices 28, 30, 32, 34(respectively). Examples of storage device 16 may include but are notlimited to: a hard disk drive; a RAID device; a random access memory(RAM); a read-only memory (ROM); and all forms of flash memory storagedevices.

Examples of client electronic devices 28, 30, 32, 34 may include, butare not limited to, data-enabled, cellular telephone 28, laptop computer30, personal digital assistant 32, personal computer 34, a notebookcomputer (not shown), a server computer (not shown), a gaming console(not shown), a smart television (not shown), and a dedicated networkdevice (not shown). Client electronic devices 28, 30, 32, 34 may eachexecute an operating system, examples of which may include but are notlimited to Microsoft Windows™, Android™, WebOS™, iOS™, Redhat Linux™, ora custom operating system.

Users 36, 38, 40, 42 may access threat mitigation process 10 directlythrough network 14 or through secondary network 18. Further, threatmitigation process 10 may be connected to network 14 through secondarynetwork 18, as illustrated with link line 44.

The various client electronic devices (e.g., client electronic devices28, 30, 32, 34) may be directly or indirectly coupled to network 14 (ornetwork 18). For example, data-enabled, cellular telephone 28 and laptopcomputer 30 are shown wirelessly coupled to network 14 via wirelesscommunication channels 46, 48 (respectively) established betweendata-enabled, cellular telephone 28, laptop computer 30 (respectively)and cellular network/bridge 50, which is shown directly coupled tonetwork 14. Further, personal digital assistant 32 is shown wirelesslycoupled to network 14 via wireless communication channel 52 establishedbetween personal digital assistant 32 and wireless access point (i.e.,WAP) 54, which is shown directly coupled to network 14. Additionally,personal computer 34 is shown directly coupled to network 18 via ahardwired network connection.

WAP 54 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, 802.11n,Wi-Fi, and/or Bluetooth device that is capable of establishing wirelesscommunication channel 52 between personal digital assistant 32 and WAP54. As is known in the art, IEEE 802.11x specifications may use Ethernetprotocol and carrier sense multiple access with collision avoidance(i.e., CSMA/CA) for path sharing. The various 802.11x specifications mayuse phase-shift keying (i.e., PSK) modulation or complementary codekeying (i.e., CCK) modulation, for example. As is known in the art,Bluetooth is a telecommunications industry specification that allowse.g., mobile phones, computers, and personal digital assistants to beinterconnected using a short-range wireless connection.

Artificial Intelligence/Machines Learning Overview:

Assume for illustrative purposes that threat mitigation process 10includes probabilistic process 56 (e.g., an artificialintelligence/machine learning process) that is configured to processinformation (e.g., information 58). As will be discussed below ingreater detail, examples of information 58 may include but are notlimited to platform information (e.g., structured or unstructuredcontent) being scanned to detect security events (e.g., access auditing;anomalies; authentication; denial of services; exploitation; malware;phishing; spamming; reconnaissance; and/or web attack) within amonitored computing platform (e.g., computing platform 60).

As is known in the art, structured content may be content that isseparated into independent portions (e.g., fields, columns, features)and, therefore, may have a pre-defined data model and/or is organized ina pre-defined manner. For example, if the structured content concerns anemployee list: a first field, column or feature may define the firstname of the employee; a second field, column or feature may define thelast name of the employee; a third field, column or feature may definethe home address of the employee; and a fourth field, column or featuremay define the hire date of the employee.

Further and as is known in the art, unstructured content may be contentthat is not separated into independent portions (e.g., fields, columns,features) and, therefore, may not have a pre-defined data model and/oris not organized in a pre-defined manner. For example, if theunstructured content concerns the same employee list: the first name ofthe employee, the last name of the employee, the home address of theemployee, and the hire date of the employee may all be combined into onefield, column or feature.

For the following illustrative example, assume that information 58 isunstructured content, an example of which may include but is not limitedto unstructured user feedback received by a company (e.g., text-basedfeedback such as text-messages, social media posts, and email messages;and transcribed voice-based feedback such as transcribed voice mail, andtranscribed voice messages).

When processing information 58, probabilistic process 56 may useprobabilistic modeling to accomplish such processing, wherein examplesof such probabilistic modeling may include but are not limited todiscriminative modeling, generative modeling, or combinations thereof.

As is known in the art, probabilistic modeling may be used within modernartificial intelligence systems (e.g., probabilistic process 56), inthat these probabilistic models may provide artificial intelligencesystems with the tools required to autonomously analyze vast quantitiesof data (e.g., information 58).

Examples of the tasks for which probabilistic modeling may be utilizedmay include but are not limited to.

-   -   predicting media (music, movies, books) that a user may like or        enjoy based upon media that the user has liked or enjoyed in the        past;    -   transcribing words spoken by a user into editable text;    -   grouping genes into gene clusters;    -   identifying recurring patterns within vast data sets;    -   filtering email that is believed to be spam from a user's inbox;    -   generating clean (i.e., non-noisy) data from a noisy data set;    -   analyzing (voice-based or text-based) customer feedback; and    -   diagnosing various medical conditions and diseases.

For each of the above-described applications of probabilistic modeling,an initial probabilistic model may be defined, wherein this initialprobabilistic model may be subsequently (e.g., iteratively orcontinuously) modified and revised, thus allowing the probabilisticmodels and the artificial intelligence systems (e.g., probabilisticprocess 56) to “learn” so that future probabilistic models may be moreprecise and may explain more complex data sets.

Accordingly, probabilistic process 56 may define an initialprobabilistic model for accomplishing a defined task (e.g., theanalyzing of information 58). For the illustrative example, assume thatthis defined task is analyzing customer feedback (e.g., information 58)that is received from customers of e.g., store 62 via an automatedfeedback phone line. For this example, assume that information 58 isinitially voice-based content that is processed via e.g., aspeech-to-text process that results in unstructured text-based customerfeedback (e.g., information 58).

With respect to probabilistic process 56, a probabilistic model may beutilized to go from initial observations about information 58 (e.g., asrepresented by the initial branches of a probabilistic model) toconclusions about information 58 (e.g., as represented by the leaves ofa probabilistic model).

As used in this disclosure, the term “branch” may refer to the existence(or non-existence) of a component (e.g., a sub-model) of (or includedwithin) a model. Examples of such a branch may include but are notlimited to: an execution branch of a probabilistic program or othergenerative model, a part (or parts) of a probabilistic graphical model,and/or a component neural network that may (or may not) have beenpreviously trained.

While the following discussion provides a detailed example of aprobabilistic model, this is for illustrative purposes only and is notintended to be a limitation of this disclosure, as other configurationsare possible and are considered to be within the scope of thisdisclosure. For example, the following discussion may concern any typeof model (e.g., be it probabilistic or other) and, therefore, thebelow-described probabilistic model is merely intended to be oneillustrative example of a type of model and is not intended to limitthis disclosure to probabilistic models.

Additionally, while the following discussion concerns word-based routingof messages through a probabilistic model, this is for illustrativepurposes only and is not intended to be a limitation of this disclosure,as other configurations are possible and are considered to be within thescope of this disclosure. Examples of other types of information thatmay be used to route messages through a probabilistic model may include:the order of the words within a message; and the punctuationinterspersed throughout the message.

For example and referring also to FIG. 2, there is shown one simplifiedexample of a probabilistic model (e.g., probabilistic model 100) thatmay be utilized to analyze information 58 (e.g., unstructured text-basedcustomer feedback) concerning store 62. The manner in whichprobabilistic model 100 may be automatically-generated by probabilisticprocess 56 will be discussed below in detail. In this particularexample, probabilistic model 100 may receive information 58 (e.g.,unstructured text-based customer feedback) at branching node 102 forprocessing. Assume that probabilistic model 100 includes four branchesoff of branching node 102, namely: service branch 104; selection branch106; location branch 108; and value branch 110 that respectively lead toservice node 112, selection node 114, location node 116, and value node118.

As stated above, service branch 104 may lead to service node 112, whichmay be configured to process the portion of information 58 (e.g.,unstructured text-based customer feedback) that concerns (in whole or inpart) feedback concerning the customer service of store 62. For example,service node 112 may define service word list 120 that may include e.g.,the word service, as well as synonyms of (and words related to) the wordservice (e.g., cashier, employee, greeter and manager). Accordingly andin the event that a portion of information 58 (e.g., a text-basedcustomer feedback message) includes the word cashier, employee, greeterand/or manager, that portion of information 58 may be considered to betext-based customer feedback concerning the service received at store 62and (therefore) may be routed to service node 112 of probabilistic model100 for further processing. Assume for this illustrative example thatprobabilistic model 100 includes two branches off of service node 112,namely: good service branch 122 and bad service branch 124.

Good service branch 122 may lead to good service node 126, which may beconfigured to process the portion of information 58 (e.g., unstructuredtext-based customer feedback) that concerns (in whole or in part) goodfeedback concerning the customer service of store 62. For example, goodservice node 126 may define good service word list 128 that may includee.g., the word good, as well as synonyms of (and words related to) theword good (e.g., courteous, friendly, lovely, happy, and smiling).Accordingly and in the event that a portion of information 58 (e.g., atext-based customer feedback message) that was routed to service node112 includes the word good, courteous, friendly, lovely, happy, and/orsmiling, that portion of information 58 may be considered to betext-based customer feedback indicative of good service received atstore 62 (and, therefore, may be routed to good service node 126).

Bad service branch 124 may lead to bad service node 130, which may beconfigured to process the portion of information 58 (e.g., unstructuredtext-based customer feedback) that concerns (in whole or in part) badfeedback concerning the customer service of store 62. For example, badservice node 130 may define bad service word list 132 that may includee.g., the word bad, as well as synonyms of (and words related to) theword bad (e.g., rude, mean, jerk, miserable, and scowling). Accordinglyand in the event that a portion of information 58 (e.g., a text-basedcustomer feedback message) that was routed to service node 112 includesthe word bad, rude, mean, jerk, miserable, and/or scowling, that portionof information 58 may be considered to be text-based customer feedbackindicative of bad service received at store 62 (and, therefore, may berouted to bad service node 130).

As stated above, selection branch 106 may lead to selection node 114,which may be configured to process the portion of information 58 (e.g.,unstructured text-based customer feedback) that concerns (in whole or inpart) feedback concerning the selection available at store 62. Forexample, selection node 114 may define selection word list 134 that mayinclude e.g., words indicative of the selection available at store 62.Accordingly and in the event that a portion of information 58 (e.g., atext-based customer feedback message) includes any of the words definedwithin selection word list 134, that portion of information 58 may beconsidered to be text-based customer feedback concerning the selectionavailable at store 62 and (therefore) may be routed to selection node114 of probabilistic model 100 for further processing. Assume for thisillustrative example that probabilistic model 100 includes two branchesoff of selection node 114, namely: good selection branch 136 and badselection branch 138.

Good selection branch 136 may lead to good selection node 140, which maybe configured to process the portion of information 58 (e.g.,unstructured text-based customer feedback) that concerns (in whole or inpart) good feedback concerning the selection available at store 62. Forexample, good selection node 140 may define good selection word list 142that may include words indicative of a good selection at store 62.Accordingly and in the event that a portion of information 58 (e.g., atext-based customer feedback message) that was routed to selection node114 includes any of the words defined within good selection word list142, that portion of information 58 may be considered to be text-basedcustomer feedback indicative of a good selection available at store 62(and, therefore, may be routed to good selection node 140).

Bad selection branch 138 may lead to bad selection node 144, which maybe configured to process the portion of information 58 (e.g.,unstructured text-based customer feedback) that concerns (in whole or inpart) bad feedback concerning the selection available at store 62. Forexample, bad selection node 144 may define bad selection word list 146that may include words indicative of a bad selection at store 62.Accordingly and in the event that a portion of information 58 (e.g., atext-based customer feedback message) that was routed to selection node114 includes any of the words defined within bad selection word list146, that portion of information 58 may be considered to be text-basedcustomer feedback indicative of a bad selection being available at store62 (and, therefore, may be routed to bad selection node 144).

As stated above, location branch 108 may lead to location node 116,which may be configured to process the portion of information 58 (e.g.,unstructured text-based customer feedback) that concerns (in whole or inpart) feedback concerning the location of store 62. For example,location node 116 may define location word list 148 that may includee.g., words indicative of the location of store 62. Accordingly and inthe event that a portion of information 58 (e.g., a text-based customerfeedback message) includes any of the words defined within location wordlist 148, that portion of information 58 may be considered to betext-based customer feedback concerning the location of store 62 and(therefore) may be routed to location node 116 of probabilistic model100 for further processing. Assume for this illustrative example thatprobabilistic model 100 includes two branches off of location node 116,namely: good location branch 150 and bad location branch 152.

Good location branch 150 may lead to good location node 154, which maybe configured to process the portion of information 58 (e.g.,unstructured text-based customer feedback) that concerns (in whole or inpart) good feedback concerning the location of store 62. For example,good location node 154 may define good location word list 156 that mayinclude words indicative of store 62 being in a good location.Accordingly and in the event that a portion of information 58 (e.g., atext-based customer feedback message) that was routed to location node116 includes any of the words defined within good location word list156, that portion of information 58 may be considered to be text-basedcustomer feedback indicative of store 62 being in a good location (and,therefore, may be routed to good location node 154).

Bad location branch 152 may lead to bad location node 158, which may beconfigured to process the portion of information 58 (e.g., unstructuredtext-based customer feedback) that concerns (in whole or in part) badfeedback concerning the location of store 62. For example, bad locationnode 158 may define bad location word list 160 that may include wordsindicative of store 62 being in a bad location. Accordingly and in theevent that a portion of information 58 (e.g., a text-based customerfeedback message) that was routed to location node 116 includes any ofthe words defined within bad location word list 160, that portion ofinformation 58 may be considered to be text-based customer feedbackindicative of store 62 being in a bad location (and, therefore, may berouted to bad location node 158).

As stated above, value branch 110 may lead to value node 118, which maybe configured to process the portion of information 58 (e.g.,unstructured text-based customer feedback) that concerns (in whole or inpart) feedback concerning the value received at store 62. For example,value node 118 may define value word list 162 that may include e.g.,words indicative of the value received at store 62. Accordingly and inthe event that a portion of information 58 (e.g., a text-based customerfeedback message) includes any of the words defined within value wordlist 162, that portion of information 58 may be considered to betext-based customer feedback concerning the value received at store 62and (therefore) may be routed to value node 118 of probabilistic model100 for further processing. Assume for this illustrative example thatprobabilistic model 100 includes two branches off of value node 118,namely: good value branch 164 and bad value branch 166.

Good value branch 164 may lead to good value node 168, which may beconfigured to process the portion of information 58 (e.g., unstructuredtext-based customer feedback) that concerns (in whole or in part) goodvalue being received at store 62. For example, good value node 168 maydefine good value word list 170 that may include words indicative ofreceiving good value at store 62. Accordingly and in the event that aportion of information 58 (e.g., a text-based customer feedback message)that was routed to value node 118 includes any of the words definedwithin good value word list 170, that portion of information 58 may beconsidered to be text-based customer feedback indicative of good valuebeing received at store 62 (and, therefore, may be routed to good valuenode 168).

Bad value branch 166 may lead to bad value node 172, which may beconfigured to process the portion of information 58 (e.g., unstructuredtext-based customer feedback) that concerns (in whole or in part) badvalue being received at store 62. For example, bad value node 172 maydefine bad value word list 174 that may include words indicative ofreceiving bad value at store 62. Accordingly and in the event that aportion of information 58 (e.g., a text-based customer feedback message)that was routed to value node 118 includes any of the words definedwithin bad value word list 174, that portion of information 58 may beconsidered to be text-based customer feedback indicative of bad valuebeing received at store 62 (and, therefore, may be routed to bad valuenode 172).

Once it is established that good or bad customer feedback was receivedconcerning store 62 (i.e., with respect to the service, the selection,the location or the value), representatives and/or agents of store 62may address the provider of such good or bad feedback via e.g., socialmedia postings, text-messages and/or personal contact.

Assume for illustrative purposes that user 36 uses data-enabled,cellular telephone 28 to provide feedback 64 (e.g., a portion ofinformation 58) to an automated feedback phone line concerning store 62.Upon receiving feedback 64 for analysis, probabilistic process 56 mayidentify any pertinent content that is included within feedback 64.

For illustrative purposes, assume that user 36 was not happy with theirexperience at store 62 and that feedback 64 provided by user 36 was “mycashier was rude and the weather was rainy”. Accordingly and for thisexample, probabilistic process 56 may identify the pertinent content(included within feedback 64) as the phrase “my cashier was rude” andmay ignore/remove the irrelevant content “the weather was rainy”. As (inthis example) feedback 64 includes the word “cashier”, probabilisticprocess 56 may route feedback 64 to service node 112 via service branch104. Further, as feedback 64 also includes the word “rude”,probabilistic process 56 may route feedback 64 to bad service node 130via bad service branch 124 and may consider feedback 64 to be text-basedcustomer feedback indicative of bad service being received at store 62.

For further illustrative purposes, assume that user 36 was happy withtheir experience at store 62 and that feedback 64 provided by user 36was “the clothing I purchased was classy but my cab got stuck intraffic”. Accordingly and for this example, probabilistic process 56 mayidentify the pertinent content (included within feedback 64) as thephrase “the clothing I purchased was classy” and may ignore/remove theirrelevant content “my cab got stuck in traffic”. As (in this example)feedback 64 includes the word “clothing”, probabilistic process 56 mayroute feedback 64 to selection node 114 via selection branch 106.Further, as feedback 64 also includes the word “classy”, probabilisticprocess 56 may route feedback 64 to good selection node 140 via goodselection branch 136 and may consider feedback 64 to be text-basedcustomer feedback indicative of a good selection being available atstore 62.

Model Generation Overview:

While the following discussion concerns the automated generation of aprobabilistic model, this is for illustrative purposes only and is notintended to be a limitation of this disclosure, as other configurationsare possible and are considered to be within the scope of thisdisclosure. For example, the following discussion of automatedgeneration may be utilized on any type of model. For example, thefollowing discussion may be applicable to any other form ofprobabilistic model or any form of generic model (such as DempsterShaffer theory or fuzzy logic).

As discussed above, probabilistic model 100 may be utilized tocategorize information 58, thus allowing the various messages includedwithin information 58 to be routed to (in this simplified example) oneof eight nodes (e.g., good service node 126, bad service node 130, goodselection node 140, bad selection node 144, good location node 154, badlocation node 158, good value node 168, and bad value node 172). For thefollowing example, assume that store 62 is a long-standing and wellestablished shopping establishment. Further, assume that information 58is a very large quantity of voice mail messages (>10,000 messages) thatwere left by customers of store 62 on a voice-based customer feedbackline. Additionally, assume that this very large quantity of voice mailmessages (>10,000) have been transcribed into a very large quantity oftext-based messages (>10,000).

Probabilistic process 56 may be configured to automatically defineprobabilistic model 100 based upon information 58. Accordingly,probabilistic process 56 may receive content (e.g., a very largequantity of text-based messages) and may be configured to define one ormore probabilistic model variables for probabilistic model 100. Forexample, probabilistic process 56 may be configured to allow a user tospecify such probabilistic model variables. Another example of suchvariables may include but is not limited to values and/or ranges ofvalues for a data flow variable. For the following discussion and forthis disclosure, examples of a “variable” may include but are notlimited to variables, parameters, ranges, branches and nodes.

Specifically and for this example, assume that probabilistic process 56defines the initial number of branches (i.e., the number of branches offof branching node 102) within probabilistic model 100 as four (i.e.,service branch 104, selection branch 106, location branch 108 and valuebranch 110). The defining of the initial number of branches (i.e., thenumber of branches off of branching node 102) within probabilistic model100 as four may be effectuated in various ways (e.g., manually oralgorithmically). Further and when defining probabilistic model 100based, at least in part, upon information 58 and the one or more modelvariables (i.e., defining the number of branches off of branching node102 as four), probabilistic process 56 may process information 58 toidentify the pertinent content included within information 58. Asdiscussed above, probabilistic process 56 may identify the pertinentcontent (included within information 58) and may ignore/remove theirrelevant content.

This type of processing of information 58 may continue for all of thevery large quantity of text-based messages (>10,000) included withininformation 58. And using the probabilistic modeling technique describedabove, probabilistic process 56 may define a first version of theprobabilistic model (e.g., probabilistic model 100) based, at least inpart, upon pertinent content found within information 58. Accordingly, afirst text-based message included within information 58 may be processedto extract pertinent information from that first message, wherein thispertinent information may be grouped in a manner to correspond (at leasttemporarily) with the requirement that four branches originate frombranching node 102 (as defined above).

As probabilistic process 56 continues to process information 58 toidentify pertinent content included within information 58, probabilisticprocess 56 may identify patterns within these text-based messageincluded within information 58. For example, the messages may allconcern one or more of the service, the selection, the location and/orthe value of store 62. Further and e.g., using the probabilisticmodeling technique described above, probabilistic process 56 may processinformation 58 to e.g.: a) sort text-based messages concerning theservice into positive or negative service messages; b) sort text-basedmessages concerning the selection into positive or negative selectionmessages; c) sort text-based messages concerning the location intopositive or negative location messages; and/or d) sort text-basedmessages concerning the value into positive or negative servicemessages. For example, probabilistic process 56 may define various lists(e.g., lists 128, 132, 142, 146, 156, 160, 170, 174) by starting with aroot word (e.g., good or bad) and may then determine synonyms for thesewords and use those words and synonyms to populate lists 128, 132, 142,146, 156, 160, 170, 174.

Continuing with the above-stated example, once information 58 (or aportion thereof) is processed by probabilistic process 56, probabilisticprocess 56 may define a first version of the probabilistic model (e.g.,probabilistic model 100) based, at least in part, upon pertinent contentfound within information 58. Probabilistic process 56 may compare thefirst version of the probabilistic model (e.g., probabilistic model 100)to information 58 to determine if the first version of the probabilisticmodel (e.g., probabilistic model 100) is a good explanation of thecontent.

When determining if the first version of the probabilistic model (e.g.,probabilistic model 100) is a good explanation of the content,probabilistic process 56 may use an ML algorithm to fit the firstversion of the probabilistic model (e.g., probabilistic model 100) tothe content, wherein examples of such an ML algorithm may include butare not limited to one or more of: an inferencing algorithm, a learningalgorithm, an optimization algorithm, and a statistical algorithm.

For example and as is known in the art, probabilistic model 100 may beused to generate messages (in addition to analyzing them). For exampleand when defining a first version of the probabilistic model (e.g.,probabilistic model 100) based, at least in part, upon pertinent contentfound within information 58, probabilistic process 56 may define aweight for each branch within probabilistic model 100 based uponinformation 58. For example, threat mitigation process 10 may equallyweight each of branches 104, 106, 108, 110 at 25%. Alternatively, ife.g., a larger percentage of information 58 concerned the servicereceived at store 62, threat mitigation process 10 may equally weighteach of branches 106, 108, 110 at 20%, while more heavily weightingbranch 104 at 40%.

Accordingly and when probabilistic process 56 compares the first versionof the probabilistic model (e.g., probabilistic model 100) toinformation 58 to determine if the first version of the probabilisticmodel (e.g., probabilistic model 100) is a good explanation of thecontent, probabilistic process 56 may generate a very large quantity ofmessages e.g., by auto-generating messages using the above-describedprobabilities, the above-described nodes & node types, and the wordsdefined in the above-described lists (e.g., lists 128, 132, 142, 146,156, 160, 170, 174), thus resulting in generated information 58′.Generated information 58′ may then be compared to information 58 todetermine if the first version of the probabilistic model (e.g.,probabilistic model 100) is a good explanation of the content. Forexample, if generated information 58′ exceeds a threshold level ofsimilarity to information 58, the first version of the probabilisticmodel (e.g., probabilistic model 100) may be deemed a good explanationof the content. Conversely, if generated information 58′ does not exceeda threshold level of similarity to information 58, the first version ofthe probabilistic model (e.g., probabilistic model 100) may be deemednot a good explanation of the content.

If the first version of the probabilistic model (e.g., probabilisticmodel 100) is not a good explanation of the content, probabilisticprocess 56 may define a revised version of the probabilistic model(e.g., revised probabilistic model 100′). When defining revisedprobabilistic model 100′, probabilistic process 56 may e.g., adjustweighting, adjust probabilities, adjust node counts, adjust node types,and/or adjust branch counts to define the revised version of theprobabilistic model (e.g., revised probabilistic model 100′). Oncedefined, the above-described process of auto-generating messages (thistime using revised probabilistic model 100′) may be repeated and thisnewly-generated content (e.g., generated information 58″) may becompared to information 58 to determine if e.g., revised probabilisticmodel 100′ is a good explanation of the content. If revisedprobabilistic model 100′ is not a good explanation of the content, theabove-described process may be repeated until a proper probabilisticmodel is defined.

The Threat Mitigation Process

As discussed above, threat mitigation process 10 may includeprobabilistic process 56 (e.g., an artificial intelligence/machinelearning process) that may be configured to process information (e.g.,information 58), wherein examples of information 58 may include but arenot limited to platform information (e.g., structured or unstructuredcontent) that may be scanned to detect security events (e.g., accessauditing, anomalies; authentication; denial of services, exploitation;malware; phishing; spamming; reconnaissance; and/or web attack) within amonitored computing platform (e.g., computing platform 60).

Referring also to FIG. 3, the monitored computing platform (e.g.,computing platform 60) utilized by business today may be a highlycomplex, multi-location computing system/network that may span multiplebuildings/locations/countries. For this illustrative example, themonitored computing platform (e.g., computing platform 60) is shown toinclude many discrete computing devices, examples of which may includebut are not limited to: server computers (e.g., server computers 200,202), desktop computers (e.g., desktop computer 204), and laptopcomputers (e.g., laptop computer 206), all of which may be coupledtogether via a network (e.g., network 208), such as an Ethernet network.Computing platform 60 may be coupled to an external network (e.g.,Internet 210) through WAF (i.e., Web Application Firewall) 212. Awireless access point (e.g., WAP 214) may be configured to allowwireless devices (e.g., smartphone 216) to access computing platform 60.Computing platform 60 may include various connectivity devices thatenable the coupling of devices within computing platform 60, examples ofwhich may include but are not limited to: switch 216, router 218 andgateway 220. Computing platform 60 may also include various storagedevices (e.g., NAS 222), as well as functionality (e.g., API Gateway224) that allows software applications to gain access to one or moreresources within computing platform 60.

In addition to the devices and functionality discussed above, othertechnology (e.g., security-relevant subsystems 226) may be deployedwithin computing platform 60 to monitor the operation of (and theactivity within) computing platform 60. Examples of security-relevantsubsystems 226 may include but are not limited to: CDN (i.e., ContentDelivery Network) systems; DAM (i.e., Database Activity Monitoring)systems; UBA (i.e., User Behavior Analytics) systems; MDM (i.e., MobileDevice Management) systems; IAM (i.e., Identity and Access Management)systems; DNS (i.e., Domain Name Server) systems, antivirus systems,operating systems, data lakes; data logs; security-relevant softwareapplications; security-relevant hardware systems; and resources externalto the computing platform.

Each of security-relevant subsystems 226 may monitor and log theiractivity with respect to computing platform 60, resulting in thegeneration of platform information 228. For example, platforminformation 228 associated with a client-defined MDM (i.e., MobileDevice Management) system may monitor and log the mobile devices thatwere allowed access to computing platform 60.

Further, SEIM (i.e., Security Information and Event Management) system230 may be deployed within computing platform 60. As is known in theart, SIEM system 230 is an approach to security management that combinesSIM (security information management) functionality and SEM (securityevent management) functionality into one security management system. Theunderlying principles of a SIEM system is to aggregate relevant datafrom multiple sources, identify deviations from the norm and takeappropriate action. For example, when a security event is detected, SIEMsystem 230 might log additional information, generate an alert andinstruct other security controls to mitigate the security event.Accordingly, SIEM system 230 may be configured to monitor and log theactivity of security-relevant subsystems 226 (e.g., CDN (i.e., ContentDelivery Network) systems; DAM (i.e., Database Activity Monitoring)systems; UBA (i.e., User Behavior Analytics) systems; MDM (i.e., MobileDevice Management) systems; IAM (i.e., Identity and Access Management)systems; DNS (i.e., Domain Name Server) systems, antivirus systems,operating systems, data lakes; data logs; security-relevant softwareapplications; security-relevant hardware systems; and resources externalto the computing platform).

Computing Platform Analysis & Reporting

As will be discussed below in greater detail, threat mitigation process10 may be configured to e.g., analyze computing platform 60 and providereports to third-parties concerning the same.

Concept 1)

Referring also to FIGS. 4-6, threat mitigation process 10 may beconfigured to obtain and combine information from multiplesecurity-relevant subsystem to generate a security profile for computingplatform 60. For example, threat mitigation process 10 may obtain 300first system-defined platform information (e.g., system-defined platforminformation 232) concerning a first security-relevant subsystem (e.g.,the number of operating systems deployed) within computing platform 60and may obtain 302 at least a second system-defined platform information(e.g., system-defined platform information 234) concerning at least asecond security-relevant subsystem (e.g., the number of antivirussystems deployed) within computing platform 60.

The first system-defined platform information (e.g., system-definedplatform information 232) and the at least a second system-definedplatform information (e.g., system-defined platform information 234) maybe obtained from one or more log files defined for computing platform60.

Specifically, system-defined platform information 232 and/orsystem-defined platform information 234 may be obtained from SIEM system230, wherein (and as discussed above) SIEM system 230 may be configuredto monitor and log the activity of security-relevant subsystems 226(e.g., CDN (i.e., Content Delivery Network) systems; DAM (i.e., DatabaseActivity Monitoring) systems; UBA (i.e., User Behavior Analytics)systems; MDM (i.e., Mobile Device Management) systems; IAM (i.e.,Identity and Access Management) systems; DNS (i.e., Domain Name Server)systems, antivirus systems, operating systems, data lakes; data logs;security-relevant software applications; security-relevant hardwaresystems; and resources external to the computing platform).

Alternatively, the first system-defined platform information (e.g.,system-defined platform information 232) and the at least a secondsystem-defined platform information (e.g., system-defined platforminformation 234) may be obtained from the first security-relevantsubsystem (e.g., the operating systems themselves) and the at least asecond security-relevant subsystem (e.g., the antivirus systemsthemselves). Specifically, system-defined platform information 232and/or system-defined platform information 234 may be obtained directlyfrom the security-relevant subsystems (e.g., the operating systemsand/or the antivirus systems), which (as discussed above) may beconfigured to self-document their activity.

Threat mitigation process 10 may combine 308 the first system-definedplatform information (e.g., system-defined platform information 232) andthe at least a second system-defined platform information (e.g.,system-defined platform information 234) to form system-definedconsolidated platform information 236. Accordingly and in this example,system-defined consolidated platform information 236 may independentlydefine the security-relevant subsystems (e.g., security-relevantsubsystems 226) present on computing platform 60.

Threat mitigation process 10 may generate 310 a security profile (e.g.,security profile 350) based, at least in part, upon system-definedconsolidated platform information 236. Through the use of securityprofile (e.g., security profile 350), the user/owner/operator ofcomputing platform 60 may be able to see that e.g., they have a securityscore of 605 out of a possible score of 1,000, wherein the averagecustomer has a security score of 237. While security profile 350 inshown in the example to include several indicators that may enable auser to compare (in this example) computing platform 60 to othercomputing platforms, this is for illustrative purposes only and is notintended to be a limitation of this disclosure, as it is understood thatother configurations are possible and are considered to be within thescope of this disclosure.

Naturally, the format, appearance and content of security profile 350may be varied greatly depending upon the design criteria and anticipatedperformance/use of threat mitigation process 10. Accordingly, theappearance, format, completeness and content of security profile 350 isfor illustrative purposes only and is not intended to be a limitation ofthis disclosure, as other configurations are possible and are consideredto be within the scope of this disclosure. For example, content may beadded to security profile 350, removed from security profile 350, and/orreformatted within security profile 350.

Additionally, threat mitigation process 10 may obtain 312 client-definedconsolidated platform information 238 for computing platform 60 from aclient information source, examples of which may include but are notlimited to one or more client-completed questionnaires (e.g.,questionnaires 240) and/or one or more client-deployed platform monitors(e.g., client-deployed platform monitor 242, which may be configured toeffectuate SIEM functionality). Accordingly and in this example,client-defined consolidated platform information 238 may define thesecurity-relevant subsystems (e.g., security-relevant subsystems 226)that the client believes are present on computing platform 60.

When generating 310 a security profile (e.g., security profile 350)based, at least in part, upon system-defined consolidated platforminformation 236, threat mitigation process 10 may compare 314 thesystem-defined consolidated platform information (e.g., system-definedconsolidated platform information 236) to the client-definedconsolidated platform information (e.g., client-defined consolidatedplatform information 238) to define differential consolidated platforminformation 352 for computing platform 60.

Differential consolidated platform information 352 may includecomparison table 354 that e.g., compares computing platform 60 to othercomputing platforms. For example and in this particular implementationof differential consolidated platform information 352, comparison table354 is shown to include three columns, namely: security-relevantsubsystem column 356 (that identifies the security-relevant subsystemsin question); system-defined consolidated platform information column358 (that is based upon system-defined consolidated platform information236 and independently defines what security-relevant subsystems arepresent on computing platform 60); and client-defined consolidatedplatform column 360 (that is based upon client-defined platforminformation 238 and defines what security-relevant subsystems the clientbelieves are present on computing platform 60). As shown withincomparison table 354, there are considerable differences between that isactually present on computing platform 60 and what is believed to bepresent on computing platform 60 (e.g., 1 IAM system vs. 10 IAM systems;4,000 operating systems vs. 10,000 operating systems, 6 DNS systems vs.10 DNS systems; 0 antivirus systems vs. 1 antivirus system, and 90firewalls vs. 150 firewalls).

Naturally, the format, appearance and content of differentialconsolidated platform information 352 may be varied greatly dependingupon the design criteria and anticipated performance/use of threatmitigation process 10. Accordingly, the appearance, format, completenessand content of differential consolidated platform information 352 is forillustrative purposes only and is not intended to be a limitation ofthis disclosure, as other configurations are possible and are consideredto be within the scope of this disclosure. For example, content may beadded to differential consolidated platform information 352, removedfrom differential consolidated platform information 352, and/orreformatted within differential consolidated platform information 352.

Concept 2)

Referring also to FIG. 7, threat mitigation process 10 may be configuredto compare what security relevant subsystems are actually includedwithin computing platform 60 versus what security relevant subsystemswere believed to be included within computing platform 60. As discussedabove, threat mitigation process 10 may combine 308 the firstsystem-defined platform information (e.g., system-defined platforminformation 232) and the at least a second system-defined platforminformation (e.g., system-defined platform information 234) to formsystem-defined consolidated platform information 236.

Threat mitigation process 10 may obtain 400 system-defined consolidatedplatform information 236 for computing platform 60 from an independentinformation source, examples of which may include but are not limitedto: one or more log files defined for computing platform 60 (e.g., suchas those maintained by SIEM system 230); and two or moresecurity-relevant subsystems (e.g., directly from the operating systemsecurity-relevant subsystem and the antivirus security-relevantsubsystem) deployed within computing platform 60.

Further and as discussed above, threat mitigation process 10 may obtain312 client-defined consolidated platform information 238 for computingplatform 60 from a client information source, examples of which mayinclude but are not limited to one or more client-completedquestionnaires (e.g., questionnaires 240) and/or one or moreclient-deployed platform monitors (e.g., client-deployed platformmonitor 242, which may be configured to effectuate SIEM functionality).

Additionally and as discussed above, threat mitigation process 10 maycompare 402 system-defined consolidated platform information 236 toclient-defined consolidated platform information 238 to definedifferential consolidated platform information 352 for computingplatform 60, wherein differential consolidated platform information 352may include comparison table 354 that e.g., compares computing platform60 to other computing platforms.

Threat mitigation process 10 may process 404 system-defined consolidatedplatform information 236 prior to comparing 402 system-definedconsolidated platform information 236 to client-defined consolidatedplatform information 238 to define differential consolidated platforminformation 352 for computing platform 60. Specifically, threatmitigation process 10 may process 404 system-defined consolidatedplatform information 236 so that it is comparable to client-definedconsolidated platform information 238.

For example and when processing 404 system-defined consolidated platforminformation 236, threat mitigation process 10 may homogenize 406system-defined consolidated platform information 236 prior to comparing402 system-defined consolidated platform information 236 toclient-defined consolidated platform information 238 to definedifferential consolidated platform information 352 for computingplatform 60. Such homogenization 406 may result in system-definedconsolidated platform information 236 and client-defined consolidatedplatform information 238 being comparable to each other (e.g., toaccommodate for differing data nomenclatures/headers).

Further and when processing 404 system-defined consolidated platforminformation 236, threat mitigation process 10 may normalize 408system-defined consolidated platform information 236 prior to comparing402 system-defined consolidated platform information 236 toclient-defined consolidated platform information 238 to definedifferential consolidated platform information 352 for computingplatform 60 (e.g., to accommodate for data differing scales/ranges).

Concept 3)

Referring also to FIG. 8, threat mitigation process 10 may be configuredto compare what security relevant subsystems are actually includedwithin computing platform 60 versus what security relevant subsystemswere believed to be included within computing platform 60.

As discussed above, threat mitigation process 10 may obtain 400system-defined consolidated platform information 236 for computingplatform 60 from an independent information source, examples of whichmay include but are not limited to: one or more log files defined forcomputing platform 60 (e.g., such as those maintained by SIEM system230); and two or more security-relevant subsystems (e.g., directly fromthe operating system security-relevant subsystem and the antivirussecurity-relevant subsystem) deployed within computing platform 60

Further and as discussed above, threat mitigation process 10 may obtain312 client-defined consolidated platform information 238 for computingplatform 60 from a client information source, examples of which mayinclude but are not limited to one or more client-completedquestionnaires (e.g., questionnaires 240) and/or one or moreclient-deployed platform monitors (e.g., client-deployed platformmonitor 242, which may be configured to effectuate SIEM functionality).

Threat mitigation process 10 may present 450 differential consolidatedplatform information 352 for computing platform 60 to a third-party,examples of which may include but are not limited to theuser/owner/operator of computing platform 60.

Additionally and as discussed above, threat mitigation process 10 maycompare 402 system-defined consolidated platform information 236 toclient-defined consolidated platform information 238 to definedifferential consolidated platform information 352 for computingplatform 60, wherein differential consolidated platform information 352may include comparison table 354 that e.g., compares computing platform60 to other computing platforms, wherein (and as discussed above) threatmitigation process 10 may process 404 (e.g., via homogenizing 406 and/ornormalizing 408) system-defined consolidated platform information 236prior to comparing 402 system-defined consolidated platform information236 to client-defined consolidated platform information 236 to definedifferential consolidated platform information 352 for computingplatform 60.

Computing Platform Analysis & Recommendation

As will be discussed below in greater detail, threat mitigation process10 may be configured to e.g., analyze & display the vulnerabilities ofcomputing platform 60.

Concept 4)

Referring also to FIG. 9, threat mitigation process 10 may be configuredto make recommendations concerning security relevant subsystems that aremissing from computing platform 60. As discussed above, threatmitigation process 10 may obtain 500 consolidated platform informationfor computing platform 60 to identify one or more deployedsecurity-relevant subsystems 226 (e.g., CDN (i.e., Content DeliveryNetwork) systems; DAM (i.e., Database Activity Monitoring) systems; UBA(i.e., User Behavior Analytics) systems; MDM (i.e., Mobile DeviceManagement) systems; IAM (i.e., Identity and Access Management) systems;DNS (i.e., Domain Name Server) systems, antivirus systems, operatingsystems, data lakes; data logs; security-relevant software applications;security-relevant hardware systems; and resources external to thecomputing platform). This consolidated platform information may beobtained from an independent information source (e.g., such as SIEMsystem 230 that may provide system-defined consolidated platforminformation 236) and/or may be obtained from a client information source(e.g., such as questionnaires 240 that may provide client-definedconsolidated platform information 238).

Referring also to FIG. 10, threat mitigation process 10 may process 506the consolidated platform information (e.g., system-defined consolidatedplatform information 236 and/or client-defined consolidated platforminformation 238) to identify one or more non-deployed security-relevantsubsystems (within computing platform 60) and may then generate 508 alist of ranked & recommended security-relevant subsystems (e.g.,non-deployed security-relevant subsystem list 550) that ranks the one ormore non-deployed security-relevant subsystems.

For this particular illustrative example, non-deployed security-relevantsubsystem list 550 is shown to include column 552 that identifies sixnon-deployed security-relevant subsystems, namely: a CDN subsystem, aWAF subsystem, a DAM subsystem; a UBA subsystem; a API subsystem, and anMDM subsystem.

When generating 508 a list of ranked & recommended security-relevantsubsystems (e.g., non-deployed security-relevant subsystem list 550)that ranks the one or more non-deployed security-relevant subsystems,threat mitigation process 10 may rank 510 the one or more non-deployedsecurity-relevant subsystems (e.g., a CDN subsystem, a WAF subsystem, aDAM subsystem; a UBA subsystem; a API subsystem, and an MDM subsystem)based upon the anticipated use of the one or more non-deployedsecurity-relevant subsystems within computing platform 60. This ranking510 of the non-deployed security-relevant subsystems (e.g., a CDNsubsystem, a WAF subsystem, a DAM subsystem; a UBA subsystem; a APIsubsystem, and an MDM subsystem) may be agnostic in nature and may bebased on the functionality/effectiveness of the non-deployedsecurity-relevant subsystems and the anticipated manner in which theirimplementation may impact the functionality/security of computingplatform 60.

Threat mitigation process 10 may provide 512 the list of ranked &recommended security-relevant subsystems (e.g., non-deployedsecurity-relevant subsystem list 550) to a third-party, examples ofwhich may include but are not limited to a user/owner/operator ofcomputing platform 60.

Additionally, threat mitigation process 10 may identify 514 acomparative for at least one of the non-deployed security-relevantsubsystems (e.g., a CDN subsystem, a WAF subsystem, a DAM subsystem; aUBA subsystem; a API subsystem, and an MDM subsystem) defined within thelist of ranked & recommended security-relevant subsystems (e.g.,non-deployed security-relevant subsystem list 550). This comparative mayinclude vendor customers in a specific industry comparative and/orvendor customers in any industry comparative.

For example and in addition to column 552, non-deployedsecurity-relevant subsystem list 550 may include columns 554, 556 fordefining the comparatives for the six non-deployed security-relevantsubsystems, namely: a CDN subsystem, a WAF subsystem, a DAM subsystem; aUBA subsystem; a API subsystem, and an MDM subsystem. Specifically,column 554 is shown to define comparatives concerning vendor customersthat own the non-deployed security-relevant subsystems in a specificindustry (i.e., the same industry as the user/owner/operator ofcomputing platform 60). Additionally, column 556 is shown to definecomparatives concerning vendor customers that own the non-deployedsecurity-relevant subsystems in any industry (i.e., not necessarily thesame industry as the user/owner/operator of computing platform 60). Forexample and concerning the comparatives of the WAF subsystem: 33% of thevendor customers in the same industry as the user/owner/operator ofcomputing platform 60 deploy a WAF subsystem, while 71% of the vendorcustomers in any industry deploy a WAF subsystem.

Naturally, the format, appearance and content of non-deployedsecurity-relevant subsystem list 550 may be varied greatly dependingupon the design criteria and anticipated performance/use of threatmitigation process 10. Accordingly, the appearance, format, completenessand content of non-deployed security-relevant subsystem list 550 is forillustrative purposes only and is not intended to be a limitation ofthis disclosure, as other configurations are possible and are consideredto be within the scope of this disclosure. For example, content may beadded to non-deployed security-relevant subsystem list 550, removed fromnon-deployed security-relevant subsystem list 550, and/or reformattedwithin non-deployed security-relevant subsystem list 550.

Concept 5)

Referring also to FIG. 11, threat mitigation process 10 may beconfigured to compare the current capabilities to the possiblecapabilities of computing platform 60. As discussed above, threatmitigation process 10 may obtain 600 consolidated platform informationto identify current security-relevant capabilities for computingplatform 60. This consolidated platform information may be obtained froman independent information source (e.g., such as SIEM system 230 thatmay provide system-defined consolidated platform information 236) and/ormay be obtained from a client information source (e.g., such asquestionnaires 240 that may provide client-defined consolidated platforminformation 238. Threat mitigation process 10 may then determine 606possible security-relevant capabilities for computing platform 60 (i.e.,the difference between the current security-relevant capabilities ofcomputing platform 60 and the possible security-relevant capabilities ofcomputing platform 60. For example, the possible security-relevantcapabilities may concern the possible security-relevant capabilities ofcomputing platform 60 using the currently-deployed security-relevantsubsystems. Additionally/alternatively, the possible security-relevantcapabilities may concern the possible security-relevant capabilities ofcomputing platform 60 using one or more supplemental security-relevantsubsystems.

Referring also to FIG. 12 and as will be explained below, threatmitigation process 10 may generate 608 comparison information 650 thatcompares the current security-relevant capabilities of computingplatform 60 to the possible security-relevant capabilities of computingplatform 60 to identify security-relevant deficiencies. Comparisoninformation 650 may include graphical comparison information, such asmulti-axial graphical comparison information that simultaneouslyillustrates a plurality of security-relevant deficiencies.

For example, comparison information 650 may define (in this particularillustrative example) graphical comparison information that include fiveaxes (e.g. axes 652, 654, 656, 658, 660) that correspond to fiveparticular types of computer threats. Comparison information 650includes origin 662, the point at which computing platform 60 has noprotection with respect to any of the five types of computer threatsthat correspond to axes 652, 654, 656, 658, 660. Accordingly, as thecapabilities of computing platform 60 are increased to counter aparticular type of computer threat, the data point along thecorresponding axis is proportionately displaced from origin 652.

As discussed above, threat mitigation process 10 may obtain 600consolidated platform information to identify current security-relevantcapabilities for computing platform 60. Concerning such currentsecurity-relevant capabilities for computing platform 60, these currentsecurity-relevant capabilities are defined by data points 664, 666, 668,670, 672, the combination of which define bounded area 674. Bounded area674 (in this example) defines the current security-relevant capabilitiesof computing platform 60.

Further and as discussed above, threat mitigation process 10 maydetermine 606 possible security-relevant capabilities for computingplatform 60 (i.e., the difference between the current security-relevantcapabilities of computing platform 60 and the possible security-relevantcapabilities of computing platform 60.

As discussed above, the possible security-relevant capabilities mayconcern the possible security-relevant capabilities of computingplatform 60 using the currently-deployed security-relevant subsystems.For example, assume that the currently-deployed security relevantsubsystems are not currently being utilized to their full potential.Accordingly, certain currently-deployed security relevant subsystems mayhave certain features that are available but are not utilized and/ordisabled. Further, certain currently-deployed security relevantsubsystems may have expanded features available if additional licensingfees are paid. Therefore and concerning such possible security-relevantcapabilities of computing platform 60 using the currently-deployedsecurity-relevant subsystems, data points 676, 678, 680, 682, 684 maydefine bounded area 686 (which represents the full capabilities of thecurrently-deployed security-relevant subsystems within computingplatform 60).

Further and as discussed above, the possible security-relevantcapabilities may concern the possible security-relevant capabilities ofcomputing platform 60 using one or more supplemental security-relevantsubsystems. For example, assume that supplemental security-relevantsubsystems are available for the deployment within computing platform60. Therefore and concerning such possible security-relevantcapabilities of computing platform 60 using such supplementalsecurity-relevant subsystems, data points 688, 690, 692, 694, 696 maydefine bounded area 698 (which represents the total capabilities ofcomputing platform 60 when utilizing the full capabilities of thecurrently-deployed security-relevant subsystems and any supplementalsecurity-relevant subsystems).

Naturally, the format, appearance and content of comparison information650 may be varied greatly depending upon the design criteria andanticipated performance/use of threat mitigation process 10.Accordingly, the appearance, format, completeness and content ofcomparison information 650 is for illustrative purposes only and is notintended to be a limitation of this disclosure, as other configurationsare possible and are considered to be within the scope of thisdisclosure. For example, content may be added to comparison information650, removed from comparison information 650, and/or reformatted withincomparison information 650.

Concept 6)

Referring also to FIG. 13, threat mitigation process 10 may beconfigured to generate a threat context score for computing platform 60.As discussed above, threat mitigation process 10 may obtain 600consolidated platform information to identify current security-relevantcapabilities for computing platform 60. This consolidated platforminformation may be obtained from an independent information source(e.g., such as SIEM system 230 that may provide system-definedconsolidated platform information 236) and/or may be obtained from aclient information source (e.g., such as questionnaires 240 that mayprovide client-defined consolidated platform information 238. As will bediscussed below in greater detail, threat mitigation process 10 maydetermine 700 comparative platform information that identifiessecurity-relevant capabilities for a comparative platform, wherein thiscomparative platform information may concern vendor customers in aspecific industry (i.e., the same industry as the user/owner/operator ofcomputing platform 60) and/or vendor customers in any industry (i.e.,not necessarily the same industry as the user/owner/operator ofcomputing platform 60).

Referring also to FIG. 14 and as will be discussed below, threatmitigation process 10 may generate 702 comparison information 750 thatcompares the current security-relevant capabilities of computingplatform 60 to the comparative platform information determined 700 forthe comparative platform to identify a threat context indicator forcomputing platform 60, wherein comparison information 750 may includegraphical comparison information 752.

Graphical comparison information 752 (which in this particular exampleis a bar chart) may identify one or more of: a current threat contextscore 754 for a client (e.g., the user/owner/operator of computingplatform 60); a maximum possible threat context score 756 for the client(e.g., the user/owner/operator of computing platform 60); a threatcontext score 758 for one or more vendor customers in a specificindustry (i.e., the same industry as the user/owner/operator ofcomputing platform 60); and a threat context score 760 for one or morevendor customers in any industry (i.e., not necessarily the sameindustry as the user/owner/operator of computing platform 60).

Naturally, the format, appearance and content of comparison information750 may be varied greatly depending upon the design criteria andanticipated performance/use of threat mitigation process 10.Accordingly, the appearance, format, completeness and content ofcomparison information 750 is for illustrative purposes only and is notintended to be a limitation of this disclosure, as other configurationsare possible and are considered to be within the scope of thisdisclosure. For example, content may be added to comparison information750, removed from comparison information 750, and/or reformatted withincomparison information 750.

Computing Platform Monitoring & Mitigation

As will be discussed below in greater detail, threat mitigation process10 may be configured to e.g., monitor the operation and performance ofcomputing platform 60.

Concept 7)

Referring also to FIG. 15, threat mitigation process 10 may beconfigured to monitor the health of computing platform 60 and providefeedback to a third-party concerning the same. Threat mitigation process10 may obtain 800 hardware performance information 244 concerninghardware (e.g., server computers, desktop computers, laptop computers,switches, firewalls, routers, gateways, WAPs, and NASs), deployed withincomputing platform 60. Hardware performance information 244 may concernthe operation and/or functionality of one or more hardware systems(e.g., server computers, desktop computers, laptop computers, switches,firewalls, routers, gateways, WAPs, and NASs) deployed within computingplatform 60.

Threat mitigation process 10 may obtain 802 platform performanceinformation 246 concerning the operation of computing platform 60.Platform performance information 246 may concern the operation and/orfunctionality of computing platform 60.

When obtaining 802 platform performance information concerning theoperation of computing platform 60, threat mitigation process 10 may (asdiscussed above): obtain 400 system-defined consolidated platforminformation 236 for computing platform 60 from an independentinformation source (e.g., SIEM system 230); obtain 312 client-definedconsolidated platform information 238 for computing platform 60 from aclient information (e.g., questionnaires 240); and present 450differential consolidated platform information 352 for computingplatform 60 to a third-party, examples of which may include but are notlimited to the user/owner/operator of computing platform 60.

When obtaining 802 platform performance information concerning theoperation of computing platform 60, threat mitigation process 10 may (asdiscussed above): obtain 500 consolidated platform information forcomputing platform 60 to identify one or more deployed security-relevantsubsystems 226 (e.g., CDN (i.e., Content Delivery Network) systems; DAM(i.e., Database Activity Monitoring) systems; UBA (i.e., User BehaviorAnalytics) systems; MDM (i.e., Mobile Device Management) systems; IAM(i.e., Identity and Access Management) systems; DNS (i.e., Domain NameServer) systems, antivirus systems, operating systems, data lakes; datalogs; security-relevant software applications; security-relevanthardware systems; and resources external to the computing platform);process 506 the consolidated platform information (e.g., system-definedconsolidated platform information 236 and/or client-defined consolidatedplatform information 238) to identify one or more non-deployedsecurity-relevant subsystems (within computing platform 60); generate508 a list of ranked & recommended security-relevant subsystems (e.g.,non-deployed security-relevant subsystem list 550) that ranks the one ormore non-deployed security-relevant subsystems; and provide 514 the listof ranked & recommended security-relevant subsystems (e.g., non-deployedsecurity-relevant subsystem list 550) to a third-party, examples ofwhich may include but are not limited to a user/owner/operator ofcomputing platform 60.

When obtaining 802 platform performance information concerning theoperation of computing platform 60, threat mitigation process 10 may (asdiscussed above): obtain 600 consolidated platform information toidentify current security-relevant capabilities for the computingplatform; determine 606 possible security-relevant capabilities forcomputing platform 60; and generate 608 comparison information 650 thatcompares the current security-relevant capabilities of computingplatform 60 to the possible security-relevant capabilities of computingplatform 60 to identify security-relevant deficiencies.

When obtaining 802 platform performance information concerning theoperation of computing platform 60, threat mitigation process 10 may (asdiscussed above): obtain 600 consolidated platform information toidentify current security-relevant capabilities for computing platform60; determine 700 comparative platform information that identifiessecurity-relevant capabilities for a comparative platform; and generate702 comparison information 750 that compares the currentsecurity-relevant capabilities of computing platform 60 to thecomparative platform information determined 700 for the comparativeplatform to identify a threat context indicator for computing platform60.

Threat mitigation process 10 may obtain 804 application performanceinformation 248 concerning one or more applications (e.g., operatingsystems, user applications, security application, and utilityapplication) deployed within computing platform 60. Applicationperformance information 248 may concern the operation and/orfunctionality of one or more software applications (e.g., operatingsystems, user applications, security application, and utilityapplication) deployed within computing platform 60.

Referring also to FIG. 16, threat mitigation process 10 may generate 806holistic platform report (e.g., holistic platform reports 850, 852)concerning computing platform 60 based, at least in part, upon hardwareperformance information 244, platform performance information 246 andapplication performance information 248. Threat mitigation process 10may be configured to receive e.g., hardware performance information 244,platform performance information 246 and application performanceinformation 248 at regular intervals (e.g., continuously, every minute,every ten minutes, etc.).

As illustrated, holistic platform reports 850, 852 may include variouspieces of content such as e.g., thought clouds that identitytopics/issues with respect to computing platform 60, system logs thatmemorialize identified issues within computing platform 60, data sourcesproviding information to computing system 60, and so on. The holisticplatform report (e.g., holistic platform reports 850, 852) may identifyone or more known conditions concerning the computing platform: andthreat mitigation process 10 may effectuate 808 one or more remedialoperations concerning the one or more known conditions.

For example, assume that the holistic platform report (e.g., holisticplatform reports 850, 852) identifies that computing platform 60 isunder a DoS (i.e., Denial of Services) attack. In computing, adenial-of-service attack (DoS attack) is a cyber-attack in which theperpetrator seeks to make a machine or network resource unavailable toits intended users by temporarily or indefinitely disrupting services ofa host connected to the Internet. Denial of service is typicallyaccomplished by flooding the targeted machine or resource withsuperfluous requests in an attempt to overload systems and prevent someor all legitimate requests from being fulfilled.

In response to detecting such a DoS attack, threat mitigation process 10may effectuate 808 one or more remedial operations. For example and withrespect to such a DoS attack, threat mitigation process 10 mayeffectuate 808 e.g., a remedial operation that instructs WAF (i.e., WebApplication Firewall) 212 to deny all incoming traffic from theidentified attacker based upon e.g., protocols, ports or the originatingIP addresses.

Threat mitigation process 10 may also provide 810 the holistic report(e.g., holistic platform reports 850, 852) to a third-party, examples ofwhich may include but are not limited to a user/owner/operator ofcomputing platform 60.

Naturally, the format, appearance and content of the holistic platformreport (e.g., holistic platform reports 850, 852) may be varied greatlydepending upon the design criteria and anticipated performance/use ofthreat mitigation process 10. Accordingly, the appearance, format,completeness and content of the holistic platform report (e.g., holisticplatform reports 850, 852) is for illustrative purposes only and is notintended to be a limitation of this disclosure, as other configurationsare possible and are considered to be within the scope of thisdisclosure. For example, content may be added to the holistic platformreport (e.g., holistic platform reports 850, 852), removed from theholistic platform report (e.g., holistic platform reports 850, 852),and/or reformatted within the holistic platform report (e.g., holisticplatform reports 850, 852).

Concept 8)

Referring also to FIG. 17, threat mitigation process 10 may beconfigured to monitor computing platform 60 for the occurrence of asecurity event and (in the event of such an occurrence) gather artifactsconcerning the same. For example, threat mitigation process 10 maydetect 900 a security event within computing platform 60 based uponidentified suspect activity. Examples of such security events mayinclude but are not limited to: DDoS events, DoS events, phishingevents, spamming events, malware events, web attacks, and exploitationevents.

When detecting 900 a security event (e.g., DDoS events, DoS events,phishing events, spamming events, malware events, web attacks, andexploitation events) within computing platform 60 based upon identifiedsuspect activity, threat mitigation process 10 may monitor 902 aplurality of sources to identify suspect activity within computingplatform 60.

For example, assume that threat mitigation process 10 detects 900 asecurity event within computing platform 60. Specifically, assume thatthreat mitigation process 10 is monitoring 902 a plurality of sources(e.g., the various log files maintained by SIEM system 230). And bymonitoring 902 such sources, assume that threat mitigation process 10detects 900 the receipt of inbound content (via an API) from a devicehaving an IP address located in Uzbekistan; the subsequent opening of aport within WAF (i.e., Web Application Firewall) 212; and the streamingof content from a computing device within computing platform 60 throughthat recently-opened port in WAF (i.e., Web Application Firewall) 212and to a device having an IP address located in Moldova.

Upon detecting 900 such a security event within computing platform 60,threat mitigation process 10 may gather 904 artifacts (e.g., artifacts250) concerning the above-described security event. When gathering 904artifacts (e.g., artifacts 250) concerning the above-described securityevent, threat mitigation process 10 may gather 906 artifacts concerningthe security event from a plurality of sources associated with thecomputing platform, wherein examples of such plurality of sources mayinclude but are not limited to the various log files maintained by SIEMsystem 230, and the various log files directly maintained by thesecurity-relevant subsystems.

Once the appropriate artifacts (e.g., artifacts 250) are gathered 904,threat mitigation process 10 may assign 908 a threat level to theabove-described security event based, at least in part, upon theartifacts (e.g., artifacts 250) gathered 904.

When assigning 908 a threat level to the above-described security event,threat mitigation process 10 may assign 910 a threat level usingartificial intelligence/machine learning. As discussed above and withrespect to artificial intelligence/machine learning being utilized toprocess data sets, an initial probabilistic model may be defined,wherein this initial probabilistic model may be subsequently (e.g.,iteratively or continuously) modified and revised, thus allowing theprobabilistic models and the artificial intelligence systems (e.g.,probabilistic process 56) to “learn” so that future probabilistic modelsmay be more precise and may explain more complex data sets. As furtherdiscussed above, probabilistic process 56 may define an initialprobabilistic model for accomplishing a defined task (e.g., theanalyzing of information 58), wherein the probabilistic model may beutilized to go from initial observations about information 58 (e.g., asrepresented by the initial branches of a probabilistic model) toconclusions about information 58 (e.g., as represented by the leaves ofa probabilistic model). Accordingly and through the use of probabilisticprocess 56, massive data sets concerning security events may beprocessed so that a probabilistic model may be defined (and subsequentlyrevised) to assign 910 a threat level to the above-described securityevent.

Once assigned 910 a threat level, threat mitigation process 10 mayexecute 912 a remedial action plan (e., remedial action plan 252) based,at least in part, upon the assigned threat level.

For example and when executing 912 a remedial action plan, threatmitigation process 10 may allow 914 the above-described suspect activityto continue when e.g., threat mitigation process 10 assigns 908 a “low”threat level to the above-described security event (e.g., assuming thatit is determined that the user of the local computing device isstreaming video of his daughter's graduation to his parents in Moldova).

Further and when executing 912 a remedial action plan, threat mitigationprocess 10 may generate 916 a security event report (e.g., securityevent report 254) based, at least in part, upon the artifacts (e.g.,artifacts 250) gathered 904, and provide 918 the security event report(e.g., security event report 254) to an analyst (e.g., analyst 256) forfurther review when e.g., threat mitigation process 10 assigns 908 a“moderate” threat level to the above-described security event (e.g.,assuming that it is determined that while the streaming of the contentis concerning, the content is low value and the recipient is not a knownbad actor).

Further and when executing 912 a remedial action plan, threat mitigationprocess 10 may autonomously execute 920 a threat mitigation plan(shutting down the stream and closing the port) when e.g., threatmitigation process 10 assigns 908 a “severe” threat level to theabove-described security event (e.g., assuming that it is determinedthat the streaming of the content is very concerning, as the content ishigh value and the recipient is a known bad actor).

Additionally, threat mitigation process 10 may allow 922 a third-party(e.g., the user/owner/operator of computing platform 60) to manuallysearch for artifacts within computing platform 60. For example, thethird-party (e.g., the user/owner/operator of computing platform 60) maybe able to search the various information resources include withincomputing platform 60, examples of which may include but are not limitedto the various log files maintained by STEM system 230, and the variouslog files directly maintained by the security-relevant subsystems withincomputing platform 60.

Computing Platform Aggregation & Searching

As will be discussed below in greater detail, threat mitigation process10 may be configured to e.g., aggregate data sets and allow for unifiedsearch of those data sets.

Concept 9)

Referring also to FIG. 18, threat mitigation process 10 may beconfigured to consolidate multiple separate and discrete data sets toform a single, aggregated data set. For example, threat mitigationprocess 10 may establish 950 connectivity with a plurality ofsecurity-relevant subsystems (e.g., security-relevant subsystems 226)within computing platform 60. As discussed above, examples ofsecurity-relevant subsystems 226 may include but are not limited to: CDN(i.e., Content Delivery Network) systems; DAM (i.e., Database ActivityMonitoring) systems; UBA (i.e., User Behavior Analytics) systems; MDM(i.e., Mobile Device Management) systems; IAM (i.e., Identity and AccessManagement) systems; DNS (i.e., Domain Name Server) systems, Antivirussystems, operating systems, data lakes; data logs; security-relevantsoftware applications; security-relevant hardware systems; and resourcesexternal to the computing platform.

When establishing 950 connectivity with a plurality of security-relevantsubsystems, threat mitigation process 10 may utilize 952 at least oneapplication program interface (e.g., API Gateway 224) to access at leastone of the plurality of security-relevant subsystems. For example, a1^(st) API gateway may be utilized to access CDN (i.e., Content DeliveryNetwork) system; a 2^(nd) API gateway may be utilized to access DAM(i.e., Database Activity Monitoring) system; a 3_(rd) API gateway may beutilized to access UBA (i.e., User Behavior Analytics) system; a 4^(th)API gateway may be utilized to access MDM (i.e., Mobile DeviceManagement) system; a 5^(th) API gateway may be utilized to access IAM(i.e., Identity and Access Management) system; and a 6^(th) API gatewaymay be utilized to access DNS (i.e., Domain Name Server) system.

Threat mitigation process 10 may obtain 954 at least onesecurity-relevant information set (e.g., a log file) from each of theplurality of security-relevant subsystems (e.g., CDN system; DAM system;UBA system; MDM system; IAM system; and DNS system), thus definingplurality of security-relevant information sets 258. As would beexpected, plurality of security-relevant information sets 258 mayutilize a plurality of different formats and/or a plurality of differentnomenclatures. Accordingly, threat mitigation process 10 may combine 956plurality of security-relevant information sets 258 to form anaggregated security-relevant information set 260 for computing platform60.

When combining 956 plurality of security-relevant information sets 258to form aggregated security-relevant information set 260, threatmitigation process 10 may homogenize 958 plurality of security-relevantinformation sets 258 to form aggregated security-relevant informationset 260. For example, threat mitigation process 10 may process one ormore of security-relevant information sets 258 so that they all have acommon format, a common nomenclature, and/or a common structure.

Once threat mitigation process 10 combines 956 plurality ofsecurity-relevant information sets 258 to form an aggregatedsecurity-relevant information set 260 for computing platform 60, threatmitigation process 10 may enable 960 a third-party (e.g., theuser/owner/operator of computing platform 60) to access aggregatedsecurity-relevant information set 260 and/or enable 962 a third-party(e.g., the user/owner/operator of computing platform 60) to searchaggregated security-relevant information set 260.

Concept 10)

Referring also to FIG. 19, threat mitigation process 10 may beconfigured to enable the searching of multiple separate and discretedata sets using a single search operation. For example and as discussedabove, threat mitigation process 10 may establish 950 connectivity witha plurality of security-relevant subsystems (e., security-relevantsubsystems 226) within computing platform 60. As discussed above,examples of security-relevant subsystems 226 may include but are notlimited to: CDN (i.e., Content Delivery Network) systems; DAM (i.e.,Database Activity Monitoring) systems; UBA (i.e., User BehaviorAnalytics) systems; MDM (i.e., Mobile Device Management) systems; IAM(i.e., Identity and Access Management) systems; DNS (i.e., Domain NameServer) systems, Antivirus systems, operating systems, data lakes; datalogs; security-relevant software applications; security-relevanthardware systems; and resources external to the computing platform.

When establishing 950 connectivity with a plurality of security-relevantsubsystems, threat mitigation process 10 may utilize 952 at least oneapplication program interface (e.g., API Gateway 224) to access at leastone of the plurality of security-relevant subsystems. For example, a1^(st) API gateway may be utilized to access CDN (i.e., Content DeliveryNetwork) system; a 2^(nd) API gateway may be utilized to access DAM(i.e., Database Activity Monitoring) system; a 3^(rd) API gateway may beutilized to access UBA (i.e., User Behavior Analytics) system; a 4^(th)API gateway may be utilized to access MDM (i.e., Mobile DeviceManagement) system; a 5^(th) API gateway may be utilized to access IAM(i.e., Identity and Access Management) system; and a 6^(th) API gatewaymay be utilized to access DNS (i.e., Domain Name Server) system.

Threat mitigation process 10 may receive 1000 unified query 262 from athird-party (e.g., the user/owner/operator of computing platform 60)concerning the plurality of security-relevant subsystems. As discussedabove, examples of security-relevant subsystems 226 may include but arenot limited to: CDN (i.e., Content Delivery Network) systems; DAM (i.e.,Database Activity Monitoring) systems; UBA (i.e., User BehaviorAnalytics) systems; MDM (i.e., Mobile Device Management) systems; IAM(i.e., Identity and Access Management) systems; DNS (i.e., Domain NameServer) systems, Antivirus systems, operating systems, data lakes; datalogs; security-relevant software applications; security-relevanthardware systems; and resources external to the computing platform.

Threat mitigation process 10 may distribute 1002 at least a portion ofunified query 262 to the plurality of security-relevant subsystems,resulting in the distribution of plurality of queries 264 to theplurality of security-relevant subsystems. For example, assume that athird-party (e.g., the user/owner/operator of computing platform 60)wishes to execute a search concerning the activity of a specificemployee. Accordingly, the third-party (e.g., the user/owner/operator ofcomputing platform 60) may formulate the appropriate unified query(e.g., unified query 262) that defines the employee name, the computingdevice(s) of the employee, and the date range of interest. Unified query262 may then be parsed to form plurality of queries 264, wherein aspecific query (within plurality of queries 264) may be defined for eachof the plurality of security-relevant subsystems and provided to theappropriate security-relevant subsystems. For example, a 1^(st) querymay be included within plurality of queries 264 and provided to CDN(i.e., Content Delivery Network) system; a 2^(nd) query may be includedwithin plurality of queries 264 and provided to DAM (i.e., DatabaseActivity Monitoring) system; a 3^(rd) query may be included withinplurality of queries 264 and provided to UBA (i.e., User BehaviorAnalytics) system; a 4^(th) query may be included within plurality ofqueries 264 and provided to MDM (i.e., Mobile Device Management) system;a 5^(th) query may be included within plurality of queries 264 andprovided to IAM (i.e., Identity and Access Management) system; and a6^(th) query may be included within plurality of queries 264 andprovided to DNS (i.e., Domain Name Server) system.

Threat mitigation process 10 may effectuate 1004 at least a portion ofunified query 262 on each of the plurality of security-relevantsubsystems to generate plurality of result sets 266. For example, the1^(st) query may be executed on CDN (i.e., Content Delivery Network)system to produce a 1^(st) result set; the 2^(nd) query may be executedon DAM (i.e., Database Activity Monitoring) system to produce a 2^(nd)result set; the 3^(rd) query may be executed on UBA (i.e., User BehaviorAnalytics) system to produce a 3^(rd) result set; the 4^(th) query maybe executed on MDM (i.e., Mobile Device Management) system to produce a4^(th) result set; the 5^(th) query may be executed on IAM (i.e.,Identity and Access Management) system to produce a 5^(th) result set;and the 6^(th) query may executed on DNS (i.e., Domain Name Server)system to produce a 6^(th) result set.

Threat mitigation process 10 may receive 1006 plurality of result sets266 from the plurality of security-relevant subsystems. Threatmitigation process 10 may then combine 1008 plurality of result sets 266to form unified query result 268. When combining 1008 plurality ofresult sets 266 to form unified query result 268, threat mitigationprocess 10 may homogenize 1010 plurality of result sets 266 to formunified query result 268. For example, threat mitigation process 10 mayprocess one or more discrete result sets included within plurality ofresult sets 266 so that the discrete result sets within plurality ofresult sets 266 all have a common format, a common nomenclature, and/ora common structure. Threat mitigation process 10 may then provide 1012unified query result 268 to the third-party (e.g., theuser/owner/operator of computing platform 60).

Concept 11)

Referring also to FIG. 20, threat mitigation process 10 may beconfigured to utilize artificial intelligence/machine learning toautomatically consolidate multiple separate and discrete data sets toform a single, aggregated data set. For example and as discussed above,threat mitigation process 10 may establish 950 connectivity with aplurality of security-relevant subsystems (e.g., security-relevantsubsystems 226) within computing platform 60. As discussed above,examples of security-relevant subsystems 226 may include but are notlimited to: CDN (i.e., Content Delivery Network) systems; DAM (i.e.,Database Activity Monitoring) systems: UBA (i.e., User BehaviorAnalytics) systems; MDM (i.e., Mobile Device Management) systems; IAM(i.e., Identity and Access Management) systems; DNS (i.e., Domain NameServer) systems, Antivirus systems, operating systems, data lakes; datalogs; security-relevant software applications; security-relevanthardware systems; and resources external to the computing platform.

As discussed above and when establishing 950 connectivity with aplurality of security-relevant subsystems, threat mitigation process 10may utilize 952 at least one application program interface (e.g., APIGateway 224) to access at least one of the plurality ofsecurity-relevant subsystems. For example, a 1^(st) API gateway may beutilized to access CDN (i.e., Content Delivery Network) system; a 2^(nd)API gateway may be utilized to access DAM (i.e., Database ActivityMonitoring) system; a 3^(rd) API gateway may be utilized to access UBA(i.e., User Behavior Analytics) system; a 4^(th) API gateway may beutilized to access MDM (i.e., Mobile Device Management) system; a 5^(th)API gateway may be utilized to access IAM (i.e., Identity and AccessManagement) system; and a 6^(th) API gateway may be utilized to accessDNS (i.e., Domain Name Server) system.

As discussed above, threat mitigation process 10 may obtain 954 at leastone security-relevant information set (e.g., a log file) from each ofthe plurality of security-relevant subsystems (e.g., CDN system; DAMsystem; UBA system; MDM system; IAM system; and DNS system), thusdefining plurality of security-relevant information sets 258. As wouldbe expected, plurality of security-relevant information sets 258 mayutilize a plurality of different formats and/or a plurality of differentnomenclatures.

Threat mitigation process 10 may process 1050 plurality ofsecurity-relevant information sets 258 using artificial learning/machinelearning to identify one or more commonalities amongst plurality ofsecurity-relevant information sets 258. As discussed above and withrespect to artificial intelligence/machine learning being utilized toprocess data sets, an initial probabilistic model may be defined,wherein this initial probabilistic model may be subsequently (e.g.,iteratively or continuously) modified and revised, thus allowing theprobabilistic models and the artificial intelligence systems (e.g.,probabilistic process 56) to “learn” so that future probabilistic modelsmay be more precise and may explain more complex data sets. As furtherdiscussed above, probabilistic process 56 may define an initialprobabilistic model for accomplishing a defined task (e.g., theanalyzing of information 58), wherein the probabilistic model may beutilized to go from initial observations about information 58 (e.g., asrepresented by the initial branches of a probabilistic model) toconclusions about information 58 (e.g., as represented by the leaves ofa probabilistic model). Accordingly and through the use of probabilisticprocess 56, plurality of security-relevant information sets 258 may beprocessed so that a probabilistic model may be defined (and subsequentlyrevised) to identify one or more commonalities (e.g., common headers,common nomenclatures, common data ranges, common data types, commonformats, etc.) amongst plurality of security-relevant information sets258. When processing 1050 plurality of security-relevant informationsets 258 using artificial learning/machine learning to identify one ormore commonalities amongst plurality of security-relevant informationsets 258, threat mitigation process 10 may utilize 1052 a decision tree(e.g., probabilistic model 100) based, at least in part, upon one ormore previously-acquired security-relevant information sets.

Threat mitigation process 10 may combine 1054 plurality ofsecurity-relevant information sets 258 to form aggregatedsecurity-relevant information set 260 for computing platform 60 based,at least in part, upon the one or more commonalities identified.

When combining 1054 plurality of security-relevant information sets 258to form aggregated security-relevant information set 260 for computingplatform 60 based, at least in part, upon the one or more commonalitiesidentified, threat mitigation process 10 may homogenize 1056 pluralityof security-relevant information sets 258 to form aggregatedsecurity-relevant information set 260. For example, threat mitigationprocess 10 may process one or more of security-relevant information sets258 so that they all have a common format, a common nomenclature, and/ora common structure.

Once threat mitigation process 10 combines 1054 plurality ofsecurity-relevant information sets 258 to form an aggregatedsecurity-relevant information set 260 for computing platform 60, threatmitigation process 10 may enable 1058 a third-party (e.g., theuser/owner/operator of computing platform 60) to access aggregatedsecurity-relevant information set 260 and/or enable 1060 a third-party(e.g., the user/owner/operator of computing platform 60) to searchaggregated security-relevant information set 260.

Threat Event Information Updating

As will be discussed below in greater detail, threat mitigation process10 may be configured to be updated concerning threat event information.

Concept 12)

Referring also to FIG. 21, threat mitigation process 10 may beconfigured to receive updated threat event information forsecurity-relevant subsystems 226. For example, threat mitigation process10 may receive 1100 updated threat event information 270 concerningcomputing platform 60, wherein updated threat event information 270 maydefine one or more of: updated threat listings; updated threatdefinitions; updated threat methodologies; updated threat sources; andupdated threat strategies. Threat mitigation process 10 may enable 1102updated threat event information 270 for use with one or moresecurity-relevant subsystems 226 within computing platform 60. Asdiscussed above, examples of security-relevant subsystems 226 mayinclude but are not limited to: CDN (i.e., Content Delivery Network)systems; DAM (i.e., Database Activity Monitoring) systems; UBA (i.e.,User Behavior Analytics) systems; MDM (i.e., Mobile Device Management)systems; IAM (i.e., Identity and Access Management) systems; DNS (i.e.,Domain Name Server) systems, Antivirus systems, operating systems, datalakes; data logs; security-relevant software applications;security-relevant hardware systems; and resources external to thecomputing platform.

When enabling 1102 updated threat event information 270 for use with oneor more security-relevant subsystems 226 within computing platform 60,threat mitigation process 10 may install 1104 updated threat eventinformation 270 on one or more security-relevant subsystems 226 withincomputing platform 60.

Threat mitigation process 10 may retroactively apply 1106 updated threatevent information 270 to previously-generated information associatedwith one or more security-relevant subsystems 226.

When retroactively apply 1106 updated threat event information 270 topreviously-generated information associated with one or moresecurity-relevant subsystems 226, threat mitigation process 10 may:apply 1108 updated threat event information 270 to one or morepreviously-generated log files (not shown) associated with one or moresecurity-relevant subsystems 226; apply 1110 updated threat eventinformation 270 to one or more previously-generated data files (notshown) associated with one or more security-relevant subsystems 226; andapply 1112 updated threat event information 270 to one or morepreviously-generated application files (not shown) associated with oneor more security-relevant subsystems 226.

Additionally/alternatively, threat mitigation process 10 mayproactivelyapply 1114 updated threat event information 270 tonewly-generated information associated with one or moresecurity-relevant subsystems 226.

When proactively applying 1114 updated threat event information 270 tonewly-generated information associated with one or moresecurity-relevant subsystems 226, threat mitigation process 10 may:apply 1116 updated threat event information 270 to one or morenewly-generated log files (not shown) associated with one or moresecurity-relevant subsystems 226; apply 1118 updated threat eventinformation 270 to one or more newly-generated data files (not shown)associated with one or more security-relevant subsystems 226; and apply1120 updated threat event information 270 to one or more newly-generatedapplication files (not shown) associated with one or moresecurity-relevant subsystems 226.

Concept 13)

Referring also to FIG. 22, threat mitigation process 10 may beconfigured to receive updated threat event information 270 forsecurity-relevant subsystems 226. For example and as discussed above,threat mitigation process 10 may receive 1100 updated threat eventinformation 270 concerning computing platform 60, wherein updated threatevent information 270 may define one or more of: updated threatlistings; updated threat definitions; updated threat methodologies;updated threat sources; and updated threat strategies. Further and asdiscussed above, threat mitigation process 10 may enable 1102 updatedthreat event information 270 for use with one or more security-relevantsubsystems 226 within computing platform 60. As discussed above,examples of security-relevant subsystems 226 may include but are notlimited to: CDN (i.e., Content Delivery Network) systems; DAM (i.e.,Database Activity Monitoring) systems; UBA (i.e., User BehaviorAnalytics) systems; MDM (i.e., Mobile Device Management) systems; IAM(i.e., Identity and Access Management) systems; DNS (i.e., Domain NameServer) systems, Antivirus systems, operating systems, data lakes; datalogs; security-relevant software applications; security-relevanthardware systems; and resources external to the computing platform.

As discussed above and when enabling 1102 updated threat eventinformation 270 for use with one or more security-relevant subsystems226 within computing platform 60, threat mitigation process 10 mayinstall 1104 updated threat event information 270 on one or moresecurity-relevant subsystems 226 within computing platform 60.

Sometimes, it may not be convenient and/or efficient to immediatelyapply updated threat event information 270 to security-relevantsubsystems 226. Accordingly, threat mitigation process 10 may schedule1150 the application of updated threat event information 270 topreviously-generated information associated with one or moresecurity-relevant subsystems 226.

When scheduling 1150 the application of updated threat event information270 to previously-generated information associated with one or moresecurity-relevant subsystems 226, threat mitigation process 10 may:schedule 1152 the application of updated threat event information 270 toone or more previously-generated log files (not shown) associated withone or more security-relevant subsystems 226; schedule 1154 theapplication of updated threat event information 270 to one or morepreviously-generated data files (not shown) associated with one or moresecurity-relevant subsystems 226; and schedule 1156 the application ofupdated threat event information 270 to one or more previously-generatedapplication files (not shown) associated with one or moresecurity-relevant subsystems 226.

Additionally/alternatively, threat mitigation process 10 may schedule1158 the application of the updated threat event information tonewly-generated information associated with the one or moresecurity-relevant subsystems.

When scheduling 1158 the application of updated threat event information270 to newly-generated information associated with one or moresecurity-relevant subsystems 226, threat mitigation process 10 may:schedule 1160 the application of updated threat event information 270 toone or more newly-generated log files (not shown) associated with one ormore security-relevant subsystems 226, schedule 1162 the application ofupdated threat event information 270 to one or more newly-generated datafiles (not shown) associated with one or more security-relevantsubsystems 226; and schedule 1164 the application of updated threatevent information 270 to one or more newly-generated application files(not shown) associated with one or more security-relevant subsystems226.

Concept 14)

Referring also to FIGS. 23-24, threat mitigation process 10 may beconfigured to initially display analytical data, which may then bemanipulated/updated to include automation data. For example, threatmitigation process 10 may display 1200 initial security-relevantinformation 1250 that includes analytical information (e.g., thoughtcloud 1252). Examples of such analytical information may include but isnot limited to one or more of: investigative information; and huntinginformation.

Investigative Information (a portion of analytical information): Unifiedsearching and/or automated searching, such as e.g., a security eventoccurring and searches being performed to gather artifacts concerningthat security event.

Hunt Information (a portion of analytical information): Targetedsearching/investigations, such as the monitoring and cataloging of thevideos that an employee has watched or downloaded over the past 30 days.

Threat mitigation process 10 may allow 1202 a third-party (e.g., theuser/owner/operator of computing platform 60) to manipulate initialsecurity-relevant information 1250 with automation information.

Automate Information (a portion of automation): The execution of asingle (and possibly simple) action one time, such as the blocking an IPaddress from accessing computing platform 60 whenever such an attempt ismade.

Orchestrate Information (a portion of automation): The execution of amore complex batch (or series) of tasks, such as sensing an unauthorizeddownload via an API and a) shutting down the API, adding the requestingIP address to a blacklist, and closing any ports opened for therequestor.

When allowing 1202 a third-party (e.g., the user/owner/operator ofcomputing network 60) to manipulate initial security-relevantinformation 1250 with automation information, threat mitigation process10 may allow 1204 a third-party (e.g., the user/owner/operator ofcomputing network 60) to select the automation information to add toinitial security-relevant information 1250 to generate revisedsecurity-relevant information 1250′. For example and when allowing 1204a third-party (e.g., the user/owner/operator of computing network 60) toselect the automation information to add to initial security-relevantinformation 1250 to generate revised security-relevant information1250′, threat mitigation process 10 may allow 1206 the third-party(e.g., the user/owner/operator of computing network 60) to choose aspecific type of automation information from a plurality of automationinformation types.

For example, the third-party (e.g., the user/owner/operator of computingnetwork 60) may choose to add/initiate the automation information togenerate revised security-relevant information 1250′. Accordingly,threat mitigation process 10 may render selectable options (e.g.,selectable buttons 1254, 1256) that the third-party (e.g., theuser/owner/operator of computing network 60) may select to manipulateinitial security-relevant information 1250 with automation informationto generate revised security-relevant information 1250′. For thisparticular example, the third-party (e.g., the user/owner/operator ofcomputing network 60) may choose two different options to manipulateinitial security-relevant information 1250, namely: “block ip” or“search”, both of which will result in threat mitigation process 10generating 1208 revised security-relevant information 1250′ (thatincludes the above-described automation information).

When generating 1208 revised security-relevant information 1250′ (thatincludes the above-described automation information), threat mitigationprocess 10 may combine 1210 the automation information (that resultsfrom selecting “block IP” or “search”) and initial security-relevantinformation 1250 to generate and render 1212 revised security-relevantinformation 1250′.

When rendering 1212 revised security-relevant information 1250′, threatmitigation process 10 may render 1214 revised security-relevantinformation 1250′ within interactive report 1258.

Training Routine Generation and Execution

As will be discussed below in greater detail, threat mitigation process10 may be configured to allow for the manual or automatic generation oftraining routines, as well as the execution of the same.

Concept 15)

Referring also to FIG. 25, threat mitigation process 10 may beconfigured to allow for the manual generation of testing routine 272.For example, threat mitigation process 10 may define 1300 trainingroutine 272 for a specific attack (e.g., a Denial of Services attack) ofcomputing platform 60. Specifically, threat mitigation process 10 maygenerate 1302 a simulation of the specific attack (e.g., a Denial ofServices attack) by executing training routine 272 within a controlledtest environment, an example of which may include but is not limited tovirtual machine 274 executed on a computing device (e.g., computingdevice 12).

When generating 1302 a simulation of the specific attack (e.g., a Denialof Services attack) by executing training routine 272 within thecontrolled test environment (e.g., virtual machine 274), threatmitigation process 10 may render 1304 the simulation of the specificattack (e.g., a Denial of Services attack) on the controlled testenvironment (e.g., virtual machine 274).

Threat mitigation process 10 may allow 1306 a trainee (e.g., trainee276) to view the simulation of the specific attack (e.g., a Denial ofServices attack) and may allow 1308 the trainee (e.g., trainee 276) toprovide a trainee response (e.g., trainee response 278) to thesimulation of the specific attack (e.g., a Denial of Services attack).For example, threat mitigation process 10 may execute training routine272, which trainee 276 may “watch” and provide trainee response 278.

Threat mitigation process 10 may then determine 1310 the effectivenessof trainee response 278, wherein determining 1310 the effectiveness ofthe trainee response may include threat mitigation process 10 assigning1312 a grade (e.g., a letter grade or a number grade) to traineeresponse 278.

Concept 16)

Referring also to FIG. 26, threat mitigation process 10 may beconfigured to allow for the automatic generation of testing routine 272.For example, threat mitigation process 10 may utilize 1350 artificialintelligence/machine learning to define training routine 272 for aspecific attack (e.g., a Denial of Services attack) of computingplatform 60.

As discussed above and with respect to artificial intelligence/machinelearning being utilized to process data sets, an initial probabilisticmodel may be defined, wherein this initial probabilistic model may besubsequently (e.g., iteratively or continuously) modified and revised,thus allowing the probabilistic models and the artificial intelligencesystems (e.g., probabilistic process 56) to “learn” so that futureprobabilistic models may be more precise and may explain more complexdata sets. As further discussed above, probabilistic process 56 maydefine an initial probabilistic model for accomplishing a defined task(e.g., the analyzing of information 58), wherein the probabilistic modelmay be utilized to go from initial observations about information 58(e.g., as represented by the initial branches of a probabilistic model)to conclusions about information 58 (e.g., as represented by the leavesof a probabilistic model). Accordingly and through the use ofprobabilistic process 56, information may be processed so that aprobabilistic model may be defined (and subsequently revised) to definetraining routine 272 for a specific attack (e.g., a Denial of Servicesattack) of computing platform 60.

When using 1350 artificial intelligence/machine learning to definetraining routine 272 for a specific attack (e.g., a Denial of Servicesattack) of computing platform 60, threat mitigation process 10 mayprocess 1352 security-relevant information to define training routine272 for specific attack (e.g., a Denial of Services attack) of computingplatform 60. Further and when using 1350 artificial intelligence/machinelearning to define training routine 272 for a specific attack (e.g., aDenial of Services attack) of computing platform 60, threat mitigationprocess 10 may utilize 1354 security-relevant rules to define trainingroutine 272 for a specific attack (e.g., a Denial of Services attack) ofcomputing platform 60. Accordingly, security-relevant information thate.g., defines the symptoms of e.g., a Denial of Services attack andsecurity-relevant rules that define the behavior of e.g., a Denial ofServices attack may be utilized by threat mitigation process 10 whendefining training routine 272.

As discussed above, threat mitigation process 10 may generate 1302 asimulation of the specific attack (e.g., a Denial of Services attack) byexecuting training routine 272 within a controlled test environment, anexample of which may include but is not limited to virtual machine 274executed on a computing device (e.g., computing device 12.

Further and as discussed above, when generating 1302 a simulation of thespecific attack (e.g., a Denial of Services attack) by executingtraining routine 272 within the controlled test environment (e.g.,virtual machine 274), threat mitigation process 10 may render 1304 thesimulation of the specific attack (e.g., a Denial of Services attack) onthe controlled test environment (e.g., virtual machine 274).

Threat mitigation process 10 may allow 1306 a trainee (e.g., trainee276) to view the simulation of the specific attack (e.g., a Denial ofServices attack) and may allow 1308 the trainee (e.g., trainee 276) toprovide a trainee response (e.g., trainee response 278) to thesimulation of the specific attack (e.g., a Denial of Services attack).For example, threat mitigation process 10 may execute training routine272, which trainee 276 may “watch” and provide trainee response 278.

Threat mitigation process 10 may utilize 1356 artificialintelligence/machine learning to revise training routine 272 for thespecific attack (e.g., a Denial of Services attack) of computingplatform 60 based, at least in part, upon trainee response 278.

As discussed above, threat mitigation process 10 may then determine 1310the effectiveness of trainee response 278, wherein determining 1310 theeffectiveness of the trainee response may include threat mitigationprocess 10 assigning 1312 a grade (e.g., a letter grade or a numbergrade) to trainee response 278.

Concept 17)

Referring also to FIG. 27, threat mitigation process 10 may beconfigured to allow a trainee to choose their training routine. Forexample mitigation process 10 may allow 1400 a third-party (e.g., theuser/owner/operator of computing network 60) to select a trainingroutine for a specific attack (e.g., a Denial of Services attack) ofcomputing platform 60, thus defining a selected training routine. Whenallowing 1400 a third-party (e.g., the user/owner/operator of computingnetwork 60) to select a training routine for a specific attack (e.g., aDenial of Services attack) of computing platform 60, threat mitigationprocess 10 may allow 1402 the third-party (e.g., the user/owner/operatorof computing network 60) to choose a specific training routine from aplurality of available training routines. For example, the third-party(e.g., the user/owner/operator of computing network 60) may be able toselect a specific type of attack (e.g., DDoS events, DoS events,phishing events, spamming events, malware events, web attacks, andexploitation events) and/or select a specific training routine (that mayor may not disclose the specific type of attack).

Once selected, threat mitigation process 10 may analyze 1404 therequirements of the selected training routine (e.g., training routine272) to determine a quantity of entities required to effectuate theselected training routine (e.g., training routine 272), thus definingone or more required entities. For example, assume that training routine272 has three required entities (e.g., an attacked device and twoattacking devices). According, threat mitigation process 10 may generate1406 one or more virtual machines (e.g., such as virtual machine 274) toemulate the one or more required entities. In this particular example,threat mitigation process 10 may generate 1406 three virtual machines, afirst VM for the attacked device, a second VM for the first attackingdevice and a third VM for the second attacking device. As is known inthe art, a virtual machine (VM) is an virtual emulation of a physicalcomputing system. Virtual machines may be based on computerarchitectures and may provide the functionality of a physical computer,wherein their implementations may involve specialized hardware,software, or a combination thereof.

Threat mitigation process 10 may generate 1408 a simulation of thespecific attack (e.g., a Denial of Services attack) by executing theselected training routine (e.g., training routine 272). When generating1408 the simulation of the specific attack (e.g., a Denial of Servicesattack) by executing the selected training routine (e.g., trainingroutine 272), threat mitigation process 10 may render 1410 thesimulation of the specific attack (e.g., a Denial of Services attack) byexecuting the selected training routine (e.g., training routine 272)within a controlled test environment (e.g., such as virtual machine274).

As discussed above, threat mitigation process 10 may allow 1306 atrainee (e.g., trainee 276) to view the simulation of the specificattack (e.g., a Denial of Services attack) and may allow 1308 thetrainee (e.g., trainee 276) to provide a trainee response (e.g., traineeresponse 278) to the simulation of the specific attack (e.g., a Denialof Services attack). For example, threat mitigation process 10 mayexecute training routine 272, which trainee 276 may “watch” and providetrainee response 278.

Further and as discussed above, threat mitigation process 10 may thendetermine 1310 the effectiveness of trainee response 278, whereindetermining 1310 the effectiveness of the trainee response may includethreat mitigation process 10 assigning 1312 a grade (e.g., a lettergrade or a number grade) to trainee response 278.

When training is complete, threat mitigation process 10 may cease 1412the simulation of the specific attack (e.g., a Denial of Servicesattack), wherein ceasing 1412 the simulation of the specific attack(e.g., a Denial of Services attack) may include threat mitigationprocess 10 shutting down 1414 the one or more virtual machines (e.g.,the first VM for the attacked device, the second VM for the firstattacking device and the third VM for the second attacking device).

Information Routing

As will be discussed below in greater detail, threat mitigation process10 may be configured to route information based upon whether theinformation is more threat-pertinent or less threat-pertinent.

Concept 18)

Referring also to FIG. 28, threat mitigation process 10 may beconfigured to route more threat-pertinent content in a specific manner.For example, threat mitigation process 10 may receive 1450 platforminformation (e.g., log files) from a plurality of security-relevantsubsystems (e.g., security-relevant subsystems 226). As discussed above,examples of security-relevant subsystems 226 may include but are notlimited to: CDN (i.e., Content Delivery Network) systems; DAM (i.e.,Database Activity Monitoring) systems; UBA (i.e., User BehaviorAnalytics) systems; MDM (i.e., Mobile Device Management) systems; IAM(i.e., Identity and Access Management) systems; DNS (i.e., Domain NameServer) systems, Antivirus systems, operating systems, data lakes, datalogs; security-relevant software applications; security-relevanthardware systems; and resources external to the computing platform.

Threat mitigation process 10 may process 1452 this platform information(e.g., log files) to generate processed platform information. And whenprocessing 1452 this platform information (e.g., log files) to generateprocessed platform information, threat mitigation process 10 may; parse1454 the platform information (e.g., log files) into a plurality ofsubcomponents (e.g., columns, rows, etc.) to allow for compensation ofvarying formats and/or nomenclature; enrich 1456 the platforminformation (e.g., log files) by including supplemental information fromexternal information resources; and/or utilize 1458 artificialintelligence/machine learning (in the manner described above) toidentify one or more patterns/trends within the platform information(e.g., log files).

Threat mitigation process 10 may identify 1460 more threat-pertinentcontent 280 included within the processed content, wherein identifying1460 more threat-pertinent content 280 included within the processedcontent may include processing 1462 the processed content to identifyactionable processed content that may be used by a threat analysisengine (e.g., STEM system 230) for correlation purposes. Threatmitigation process 10 may route 1464 more threat-pertinent content 280to this threat analysis engine (e.g., SIEM system 230).

Concept 19)

Referring also to FIG. 29, threat mitigation process 10 may beconfigured to route less threat-pertinent content in a specific manner.For example and as discussed above, threat mitigation process 10 mayreceive 1450 platform information (e.g., log files) from a plurality ofsecurity-relevant subsystems (e.g., security-relevant subsystems 226).As discussed above, examples of security-relevant subsystems 226 mayinclude but are not limited to: CDN (i.e., Content Delivery Network)systems; DAM (i.e., Database Activity Monitoring) systems; UBA (i.e.,User Behavior Analytics) systems; MDM (i.e., Mobile Device Management)systems; IAM (i.e., Identity and Access Management) systems; DNS (i.e.,Domain Name Server) systems, Antivirus systems, operating systems, datalakes; data logs, security-relevant software applications;security-relevant hardware systems; and resources external to thecomputing platform

Further and as discussed above, threat mitigation process 10 may process1452 this platform information (e.g., log files) to generate processedplatform information. And when processing 1452 this platform information(e.g., log files) to generate processed platform information, threatmitigation process 10 may: parse 1454 the platform information (e.g.,log files) into a plurality of subcomponents (e.g., columns, rows, etc.)to allow for compensation of varying formats and/or nomenclature; enrich1456 the platform information (e.g., log files) by includingsupplemental information from external information resources; and/orutilize 1458 artificial intelligence/machine learning (in the mannerdescribed above) to identify one or more patterns/trends within theplatform information (e.g., log files).

Threat mitigation process 10 may identify 1500 less threat-pertinentcontent 282 included within the processed content, wherein identifying1500 less threat-pertinent content 282 included within the processedcontent may include processing 1502 the processed content to identifynon-actionable processed content that is not usable by a threat analysisengine (e.g., SIEM system 230) for correlation purposes. Threatmitigation process 10 may route 1504 less threat-pertinent content 282to a long term storage system (e.g., long term storage system 284).Further, threat mitigation process 10 may be configured to allow 1506 athird-party (e.g., the user/owner/operator of computing network 60) toaccess and search long term storage system 284.

Automated Analysis

As will be discussed below in greater detail, threat mitigation process10 may be configured to automatically analyze a detected security event.

Concept 20)

Referring also to FIG. 30, threat mitigation process 10 may beconfigured to automatically classify and investigate a detected securityevent. As discussed above and in response to a security event beingdetected, threat mitigation process 10 may obtain 1550 one or moreartifacts (e.g., artifacts 250) concerning the detected security event.Examples of such a detected security event may include but are notlimited to one or more of: access auditing: anomalies; authentication:denial of services; exploitation; malware; phishing; spamming;reconnaissance; and web attack. These artifacts (e.g., artifacts 250)may be obtained 1550 from a plurality of sources associated with thecomputing platform, wherein examples of such plurality of sources mayinclude but are not limited to the various log files maintained by SLEMsystem 230, and the various log files directly maintained by thesecurity-relevant subsystems

Threat mitigation process 10 may obtain 1552 artifact information (e.g.,artifact information 286) concerning the one or more artifacts (e.g.,artifacts 250), wherein artifact information 286 may be obtained frominformation resources include within (or external to) computing platform60.

For example and when obtaining 1552 artifact information 286 concerningthe one or more artifacts (e.g., artifacts 250), threat mitigationprocess 10 may obtain 1554 artifact information 286 concerning the oneor more artifacts (e.g., artifacts 250) from one or more investigationresources (such as third-party resources that may e.g., provideinformation on known bad actors).

Once the investigation is complete, threat mitigation process 10 maygenerate 1556 a conclusion (e.g., conclusion 288) concerning thedetected security event (e.g., a Denial of Services attack) based, atleast in part, upon the detected security event (e.g., a Denial ofServices attack), the one or more artifacts (e.g., artifacts 250), andartifact information 286. Threat mitigation process 10 may document 1558the conclusion (e.g., conclusion 288), report 1560 the conclusion (e.g.,conclusion 288) to a third-party (e.g., the user/owner/operator ofcomputing network 60). Further, threat mitigation process 10 may obtain1562 supplemental artifacts and artifact information (if needed tofurther the investigation).

While the system is described above as being computer-implemented, thisis for illustrative purposes only and is not intended to be a limitationof this disclosure, as other configurations are possible and areconsidered to be within the scope of this disclosure. For example, someor all of the above-described system may be implemented by a humanbeing.

General

As will be appreciated by one skilled in the art, the present disclosuremay be embodied as a method, a system, or a computer program product.Accordingly, the present disclosure may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,the present disclosure may take the form of a computer program producton a computer-usable storage medium having computer-usable program codeembodied in the medium.

Any suitable computer usable or computer readable medium may beutilized. The computer-usable or computer-readable medium may be, forexample but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, device,or propagation medium. More specific examples (a non-exhaustive list) ofthe computer-readable medium may include the following: an electricalconnection having one or more wires, a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), anoptical fiber, a portable compact disc read-only memory (CD-ROM), anoptical storage device, a transmission media such as those supportingthe Internet or an intranet, or a magnetic storage device. Thecomputer-usable or computer-readable medium may also be paper or anothersuitable medium upon which the program is printed, as the program can beelectronically captured, via, for instance, optical scanning of thepaper or other medium, then compiled, interpreted, or otherwiseprocessed in a suitable manner, if necessary, and then stored in acomputer memory. In the context of this document, a computer-usable orcomputer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The computer-usable medium may include a propagated data signal with thecomputer-usable program code embodied therewith, either in baseband oras part of a carrier wave. The computer usable program code may betransmitted using any appropriate medium, including but not limited tothe Internet, wireline, optical fiber cable, RF, etc.

Computer program code for carrying out operations of the presentdisclosure may be written in an object oriented programming languagesuch as Java, Smalltalk, C++ or the like. However, the computer programcode for carrying out operations of the present disclosure may also bewritten in conventional procedural programming languages, such as the“C” programming language or similar programming languages. The programcode may execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network/a widearea network/the Internet (e.g., network 14).

The present disclosure is described with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the disclosure. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, may be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer/special purposecomputer/other programmable data processing apparatus, such that theinstructions, which execute via the processor of the computer or otherprogrammable data processing apparatus, create means for implementingthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

These computer program instructions may also be stored in acomputer-readable memory that may direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures may illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustrations,and combinations of blocks in the block diagrams and/or flowchartillustrations, may be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

A number of implementations have been described. Having thus describedthe disclosure of the present application in detail and by reference toembodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope of thedisclosure defined in the appended claims.

1.-21. (canceled)
 22. A computer-implemented method, executed on acomputing device, comprising: receiving platform information from aplurality of security-relevant subsystems; processing the platforminformation to generate processed platform information, includingdetecting a security event; assigning a threat level to the securityevent; identifying less threat-pertinent content included within theprocessed platform information associated with the security event; androuting the less threat-pertinent content to a long term storage system.23. The computer-implemented method of claim 22 wherein processing theplatform information to generate processed platform informationincludes: parsing the platform information into a plurality ofsubcomponents to allow for compensation of varying formats and/ornomenclature.
 24. The computer-implemented method of claim 22 whereinprocessing the platform information to generate processed platforminformation includes: enriching the platform information by includingsupplemental information from external information resources.
 25. Thecomputer-implemented method of claim 22 wherein processing the platforminformation to generate processed platform information includes:utilizing artificial intelligence/machine learning to identify one ormore patterns/behaviors defined within the platform information.
 26. Thecomputer-implemented method of claim 22 wherein the plurality ofsecurity-relevant subsystems includes one or more of: a data lake; adata log; a security-relevant software application; a security-relevanthardware system; and a resource external to the computing platform. 27.The computer-implemented method of claim 22 wherein identifying lessthreat-pertinent content included within the processed platforminformation includes: processing the processed platform information toidentify non-actionable content within the processed platforminformation that is not usable by a threat analysis engine.
 28. Thecomputer-implemented method of claim 22 further comprising: allowing athird-party to access and search the long term storage system.
 29. Acomputer program product residing on a non-transitory computer readablemedium having a plurality of instructions stored thereon which, whenexecuted by a processor, cause the processor to perform operationscomprising: receiving platform information from a plurality ofsecurity-relevant subsystems; processing the platform information togenerate processed platform information, including detecting a securityevent; assigning a threat level to the security event; identifying lessthreat-pertinent content included within the processed platforminformation associated with the security event; and routing the lessthreat-pertinent content to a long term storage system.
 30. The computerprogram product of claim 29 wherein processing the platform informationto generate processed platform information includes: parsing theplatform information into a plurality of subcomponents to allow forcompensation of varying formats and/or nomenclature.
 31. The computerprogram product of claim 29 wherein processing the platform informationto generate processed platform information includes: enriching theplatform information by including supplemental information from externalinformation resources.
 32. The computer program product of claim 29wherein processing the platform information to generate processedplatform information includes: utilizing artificial intelligence/machinelearning to identify one or more patterns/behaviors defined within theplatform information.
 33. The computer program product of claim 29wherein the plurality of security-relevant subsystems includes one ormore of: a data lake; a data log; a security-relevant softwareapplication; a security-relevant hardware system; and a resourceexternal to the computing platform.
 34. The computer program product ofclaim 29 wherein identifying less threat-pertinent content includedwithin the processed platform information includes: processing theprocessed platform information to identify non-actionable content withinthe processed platform information that is not usable by a threatanalysis engine.
 35. The computer program product of claim 29 furthercomprising: allowing a third-party to access and search the long termstorage system.
 36. A computing system including a processor and memoryconfigured to perform operations comprising: receiving platforminformation from a plurality of security-relevant subsystems; processingthe platform information to generate processed platform information,including detecting a security event; assigning a threat level to thesecurity event; identifying less threat-pertinent content includedwithin the processed platform information associated with the securityevent; and routing the less threat-pertinent content to a long termstorage system.
 37. The computing system of claim 36 wherein processingthe platform information to generate processed platform informationincludes: parsing the platform information into a plurality ofsubcomponents to allow for compensation of varying formats and/ornomenclature.
 38. The computing system of claim 36 wherein processingthe platform information to generate processed platform informationincludes: enriching the platform information by including supplementalinformation from external information resources.
 39. The computingsystem of claim 36 wherein processing the platform information togenerate processed platform information includes: utilizing artificialintelligence/machine learning to identify one or more patterns/behaviorsdefined within the platform information.
 40. The computing system ofclaim 36 wherein the plurality of security-relevant subsystems includesone or more of: a data lake; a data log; a security-relevant softwareapplication; a security-relevant hardware system; and a resourceexternal to the computing platform.
 41. The computing system of claim 36wherein identifying less threat-pertinent content included within theprocessed platform information includes: processing the processedplatform information to identify non-actionable content within theprocessed platform information that is not usable by a threat analysisengine.
 42. The computing system of claim 36 further comprising:allowing a third-party to access and search the long term storagesystem.